1.0 Changes in the popularity of movies (Basic Movie Data)

1.1 Request movie IDs and movie names from TMDb

1.1.1 Request movie IDs

import requests
import pandas as pd
import random

API_KEY = '436b4ebe719b3f41c64f720254718b77'  # TMDb API KEY
url = 'https://api.themoviedb.org/3/discover/movie'

# Initialize results dictionary
yearly_movie_ids = {}

# Loop through the years 2000 to 2024
for year in range(2014, 2025):
    movie_ids = set()  # Use a set to avoid duplicate IDs
    max_results = 400  # Desired number of movies
    page_limit = 50  # Set maximum page limit

    # Get movie IDs released each year
    for page in range(1, page_limit + 1):
        params = {
            'api_key': API_KEY,
            'primary_release_year': year,
            'sort_by': 'popularity.desc',
            'page': page,
            'vote_count.gte': 10  # Only get movies with at least 10 votes
        }

        response = requests.get(url, params=params)
        data = response.json()

        if 'results' in data and data['results']:
            for movie in data['results']:
                movie_ids.add(movie['id'])  # Add movie ID to the set
                if len(movie_ids) >= max_results:  # Stop if desired number is reached
                    break
            if len(movie_ids) >= max_results:
                break
        else:
            print(f"More movies not found for the year {year}.")
            break

    # Randomly select the desired number of movie IDs
    random_movie_ids = random.sample(movie_ids, min(max_results, len(movie_ids)))
    yearly_movie_ids[year] = random_movie_ids  # Store movie IDs for each year

# Print results
for year, ids in yearly_movie_ids.items():
    print(f"Random movie IDs for the year {year}: {ids}")

# Save results to CSV file
all_ids_df = pd.DataFrame([(year, movie_id) for year, ids in yearly_movie_ids.items() for movie_id in ids], columns=['Year', 'Movie ID'])
all_ids_df.to_csv('movie_all_ids_2014_2024_tmdb.csv', index=False, encoding='utf-8-sig')

/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_14344/3204108262.py:42: DeprecationWarning: Sampling from a set deprecated
since Python 3.9 and will be removed in a subsequent version.
  random_movie_ids = random.sample(movie_ids, min(max_results, len(movie_ids)))

Random movie IDs for the year 2014: [127585, 249164, 265180, 238636, 143049, 242643, 239566, 285812, 124905, 258750, 287084, 227735, 276843, 157353, 289732, 245916, 266285, 194662, 244761, 218778, 284289, 244786, 222899, 170687, 204922, 225565, 241855, 223954, 110390, 327833, 228967, 137106, 258893, 193610, 234200, 193612, 157832, 286554, 100241, 185341, 268060, 292014, 258193, 226448, 118340, 265018, 205596, 244267, 280002, 284293, 141043, 98566, 113833, 283317, 241868, 157847, 261776, 157823, 265712, 242095, 200727, 102651, 184315, 250546, 198185, 273895, 212778, 119450, 188166, 487186, 232679, 241842, 265851, 184346, 546726, 228970, 252171, 256917, 196867, 243352, 177572, 255718, 222935, 261103, 284305, 254578, 262551, 242090, 181533, 260063, 284296, 297090, 257932, 147441, 53182, 254375, 254904, 245775, 192145, 305143, 190859, 82702, 244237, 210479, 192102, 131631, 252178, 257512, 254194, 157350, 114155, 286873, 189, 246080, 138103, 82703, 197950, 260346, 110416, 276844, 227348, 304410, 256474, 330115, 76649, 294993, 290727, 305127, 259018, 146243, 254473, 283322, 181886, 97370, 119892, 122917, 137113, 271185, 258751, 285213, 282631, 242582, 312791, 203834, 152790, 224141, 290542, 267793, 266058, 236399, 246741, 102382, 187017, 154400, 257475, 257648, 250574, 235260, 262391, 206563, 136835, 244506, 226857, 313893, 105001, 252680, 284536, 336592, 200505, 217993, 218836, 289727, 171274, 205587, 290865, 259910, 262543, 264525, 175112, 241254, 188161, 267863, 303542, 241771, 287233, 282069, 287495, 281780, 213927, 203739, 286532, 250066, 198277, 221902, 284274, 145220, 188207, 237791, 193893, 156022, 257346, 261037, 68737, 91314, 253376, 244458, 226486, 292777, 255157, 256835, 283350, 283564, 227156, 276126, 287649, 301959, 297222, 244063, 236737, 356085, 269148, 288158, 100042, 269495, 228326, 316269, 204436, 250225, 85350, 259611, 286817, 241863, 284689, 240341, 312526, 184345, 146229, 302945, 262338, 277355, 197796, 265195, 157845, 152760, 267806, 278774, 297021, 227783, 242224, 283330, 242512, 298115, 282041, 86834, 137321, 270946, 277685, 216769, 250538, 266856, 269955, 241239, 172385, 255798, 270938, 289720, 261470, 284711, 170522, 192712, 216282, 259074, 260234, 177494, 290555, 254474, 286192, 82657, 228194, 277432, 137094, 221731, 227159, 236751, 198663, 290864, 296065, 254201, 244264, 251519, 302666, 136797, 263132, 263475, 251994, 235271, 195589, 239563, 218043, 265177, 300467, 99367, 256274, 317442, 246403, 276601, 245891, 157424, 247645, 511082, 240483, 87093, 242042, 157825, 253065, 126172, 242828, 236735, 241848, 249660, 243683, 313108, 120467, 246860, 295237, 72784, 282672, 252360, 232672, 284276, 292177, 239678, 294652, 259072, 258947, 49017, 155084, 269494, 268690, 277547, 97020, 303903, 240832, 253235, 180299, 144336, 250388, 285135, 271397, 290271, 168530, 263105, 287590, 270302, 228150, 280019, 157336, 184098, 282813, 79316, 238215, 272692, 116149, 164251, 262903, 221732, 279229, 157849, 245473, 241843, 284427, 242310, 225574, 225886, 293310, 289416, 297270, 253980, 255343, 227306, 260535, 253310, 112454, 392216, 157851, 244566, 171372, 169917, 206487, 207933, 243526, 239571, 227738, 210577, 244403, 297608, 87492, 187596, 264656, 100402]
Random movie IDs for the year 2015: [372049, 323372, 287903, 243938, 350650, 321741, 249688, 239573, 340382, 360055, 265208, 323660, 76341, 288036, 336882, 291270, 218784, 283587, 309503, 285783, 354072, 356486, 300666, 332502, 365045, 266647, 254302, 335866, 273481, 321528, 207703, 222936, 399905, 241257, 347945, 293646, 318256, 306819, 310593, 323272, 282070, 201085, 94365, 225728, 256924, 325358, 267970, 339527, 360605, 86828, 274167, 227719, 87101, 387824, 333381, 277558, 245698, 284303, 325348, 264999, 361931, 293413, 276839, 308447, 333167, 331781, 238713, 290637, 241554, 312221, 354110, 364111, 321050, 314385, 10317, 210860, 308504, 354857, 298382, 318781, 249070, 296523, 298312, 338729, 309245, 298582, 295830, 157544, 308403, 253450, 150689, 359246, 357096, 314365, 308032, 320007, 286217, 307081, 347969, 166424, 228161, 263472, 302528, 99861, 223485, 182560, 287424, 257344, 317198, 290764, 257445, 300693, 135397, 322456, 283227, 304357, 284674, 228165, 274854, 278247, 294016, 290762, 256040, 335462, 257211, 283235, 306745, 363093, 159824, 238615, 350499, 329289, 261392, 318917, 276907, 348689, 181283, 350725, 241251, 334074, 167073, 297721, 243940, 368304, 250124, 351694, 351523, 304372, 333352, 150540, 301875, 336050, 271718, 308369, 273477, 315846, 326446, 254905, 268920, 332270, 347688, 282984, 198184, 360551, 337029, 328869, 356987, 328425, 146301, 347201, 332835, 354251, 377823, 206647, 188222, 346808, 292040, 256591, 310569, 245906, 205775, 351964, 335578, 264644, 131634, 361263, 287284, 357400, 372921, 300168, 374515, 245706, 333674, 359245, 314389, 339533, 309299, 325173, 343795, 272878, 301748, 245168, 177677, 256961, 342878, 280092, 294963, 360784, 332976, 356296, 315465, 321769, 347187, 360404, 333596, 299245, 370964, 266396, 296100, 336004, 344268, 227973, 339530, 365222, 348892, 334376, 234212, 349158, 228203, 375742, 277216, 251387, 323260, 228066, 370097, 254320, 326215, 348060, 254172, 320588, 228973, 312831, 105864, 330764, 174751, 297556, 254470, 271674, 280391, 332672, 299824, 277546, 102899, 361380, 345235, 86835, 334354, 158852, 355338, 244316, 329004, 295964, 373200, 349176, 310135, 348537, 303857, 140607, 355111, 336845, 283445, 214756, 329540, 373879, 296099, 323027, 318541, 353610, 352161, 203801, 336560, 232572, 483685, 254128, 351901, 157843, 293863, 302429, 292431, 230179, 277217, 334298, 358881, 253626, 321494, 270303, 301351, 258480, 316873, 347847, 272693, 253344, 346646, 375846, 291549, 355651, 336203, 300803, 257091, 347200, 338803, 347548, 321697, 228968, 309887, 248574, 347183, 301728, 266294, 309425, 332177, 317744, 324963, 326359, 264419, 325189, 297291, 309242, 252512, 157824, 316042, 324333, 336744, 331576, 362057, 348678, 262500, 271714, 168259, 256962, 275601, 326425, 342927, 293970, 273248, 261023, 318846, 319075, 274479, 339145, 296098, 257088, 337302, 355020, 216015, 192141, 299551, 76757, 309809, 201088, 301846, 293771, 334527, 273153, 250734, 361018, 263109, 258509, 324558, 364324, 286565, 362045, 390845, 253412, 280996, 314405, 274109, 328589, 333348, 308084, 228205, 347754, 211672, 356300, 287948, 382517, 328480, 359171, 342588, 252838, 294254, 281957, 307663, 323661, 243688, 338676, 328901, 333658, 360603, 333653]
Random movie IDs for the year 2016: [301365, 283552, 205584, 352498, 413337, 352695, 339526, 339397, 394723, 342521, 439466, 355066, 259694, 357786, 377154, 361159, 153518, 334524, 372754, 413198, 406429, 293767, 408270, 379291, 339405, 408567, 330459, 140300, 270010, 369552, 296360, 319888, 419566, 290098, 329440, 188927, 408381, 376812, 234004, 393559, 335778, 347126, 262841, 336455, 288171, 383121, 340676, 258230, 323676, 375012, 381902, 278154, 332872, 311324, 376290, 376004, 394223, 369557, 291328, 313922, 262504, 425094, 387893, 369033, 334533, 364116, 429918, 331313, 47933, 379170, 382581, 334522, 308531, 136799, 241259, 324560, 354282, 330483, 312669, 335791, 384798, 339419, 333667, 413543, 407375, 410126, 195590, 339984, 410199, 296524, 372411, 381890, 259695, 310119, 377263, 340945, 359412, 388399, 368031, 328387, 369885, 283378, 302156, 377447, 372519, 341012, 378385, 284052, 297761, 339408, 376565, 381693, 381262, 378485, 375794, 399173, 340485, 360249, 427342, 413052, 209112, 318850, 429450, 278924, 276624, 207932, 353979, 382598, 248705, 393277, 382190, 277834, 368940, 348893, 340402, 371645, 409122, 346672, 348089, 318121, 376867, 345920, 333484, 421928, 393457, 267192, 364433, 295699, 263341, 270487, 407887, 384664, 296096, 387938, 374671, 410718, 313369, 306947, 372751, 256569, 339116, 337674, 346651, 427008, 326423, 332567, 366514, 393519, 330070, 376166, 291805, 363676, 345009, 380111, 376659, 303858, 259693, 310131, 419446, 309886, 310307, 58431, 286567, 369883, 332979, 351339, 353069, 269149, 401698, 378462, 359628, 303991, 386861, 335797, 402298, 315664, 245913, 290595, 391757, 374473, 374205, 396330, 340674, 372058, 317557, 431808, 381284, 426889, 355506, 340677, 259316, 413736, 405314, 300669, 308266, 271969, 325133, 245703, 276905, 376570, 362227, 336011, 43074, 373314, 382399, 377897, 271110, 415214, 346685, 334541, 329833, 408620, 267935, 382511, 384345, 293452, 359784, 270774, 370464, 353326, 380565, 410118, 366170, 324786, 409502, 333371, 213681, 343611, 381255, 430384, 382591, 397717, 332411, 246655, 381341, 370755, 384682, 389053, 377264, 710832, 348668, 271706, 363579, 341006, 127380, 354859, 369300, 68735, 384737, 452251, 294272, 335796, 313297, 525686, 382322, 323675, 378018, 424656, 121856, 394759, 394374, 351819, 372962, 340666, 397243, 351809, 300671, 419116, 405882, 316152, 302946, 302401, 390584, 381418, 324668, 381356, 338189, 333385, 382602, 356305, 331583, 291870, 168098, 360606, 374052, 95610, 390497, 334543, 359483, 423502, 378108, 417028, 396535, 376660, 299687, 376866, 369299, 347031, 366924, 390734, 267860, 322240, 408866, 267193, 298584, 416148, 184341, 258489, 382512, 308529, 374461, 284564, 408360, 374856, 300673, 293660, 241258, 410117, 413666, 342473, 146198, 382663, 337703, 412520, 274870, 315011, 404378, 338544, 351211, 290250, 378064, 324670, 369698, 374853, 356298, 375355, 413658, 367206, 338766, 291356, 68730, 391698, 302699, 316727, 283366, 325789, 403064, 301804, 360814, 375366, 380225, 1017204, 407655, 332210, 329865, 359983, 331962, 333669, 396263, 410220, 399106, 223702, 323677, 364051, 316023, 345911, 376581, 402331, 293670, 321258, 334517, 374609, 291413, 302688, 412092, 408355, 328111, 382272, 366696, 433157]
Random movie IDs for the year 2017: [448779, 435800, 274855, 413362, 437291, 472454, 390054, 435601, 340027, 369192, 437081, 387426, 394830, 414770, 431821, 339987, 300665, 451997, 442056, 431530, 432985, 417870, 355547, 257785, 402897, 398924, 390043, 480572, 166426, 558144, 353491, 433498, 348389, 404733, 370663, 428288, 532794, 421447, 335988, 290512, 441168, 203835, 395458, 405775, 449574, 408647, 339877, 411840, 479753, 428707, 467012, 460846, 455411, 301355, 335360, 462181, 513434, 365942, 397415, 297160, 354556, 339259, 487047, 460229, 346364, 403119, 481871, 364689, 460793, 337676, 354287, 425972, 284053, 464566, 340270, 389015, 479718, 383785, 126889, 382597, 471968, 451955, 353616, 431819, 452030, 437670, 435577, 438857, 469722, 437103, 453127, 430214, 392044, 458083, 417644, 398174, 316029, 265339, 435129, 336000, 461078, 397422, 339404, 453354, 339692, 437033, 337339, 413998, 441531, 397852, 427564, 452507, 418680, 55341, 445993, 449664, 452910, 419430, 451644, 339988, 415842, 359940, 458506, 462883, 469877, 449503, 316154, 464889, 457840, 446791, 521494, 47971, 429467, 332283, 324849, 181808, 438478, 305470, 417830, 274857, 419479, 394117, 399057, 274862, 452773, 449755, 463602, 345922, 301337, 335984, 477447, 391713, 429189, 297762, 313943, 430447, 408266, 378111, 376134, 399055, 339846, 460508, 534171, 379149, 422766, 401246, 406990, 390056, 353070, 363992, 282035, 538751, 339967, 345914, 450322, 488621, 343674, 283591, 435707, 315635, 429200, 347984, 423453, 400136, 420950, 462650, 433251, 315837, 398818, 441701, 436391, 436274, 438747, 408648, 263115, 433422, 401544, 401513, 424488, 420245, 426265, 412547, 354912, 481840, 437633, 433247, 445954, 390062, 339964, 291276, 353486, 298250, 400106, 395993, 402529, 399019, 281338, 343668, 396371, 428449, 395991, 458353, 432131, 479957, 406994, 453364, 268531, 446829, 467342, 447759, 416186, 431562, 330947, 429733, 428399, 324552, 378236, 424998, 430231, 426238, 283984, 428493, 345915, 451156, 416477, 283995, 440249, 409617, 400617, 432836, 95608, 390061, 425134, 417678, 414425, 423087, 467248, 425336, 427900, 420648, 428081, 452558, 465078, 390053, 436120, 461714, 269795, 417320, 462677, 445030, 423899, 433808, 392982, 415381, 418078, 445040, 396422, 468202, 395834, 353569, 461053, 444090, 440597, 484133, 293167, 381288, 372343, 400928, 434119, 364733, 405965, 396940, 341013, 475946, 477776, 488623, 461763, 323368, 440021, 406997, 337170, 390051, 335788, 409421, 455656, 411405, 388284, 413644, 339403, 449132, 180863, 137116, 395992, 431572, 289222, 457837, 345938, 398181, 350312, 334521, 321612, 460790, 419835, 324542, 419680, 392207, 351460, 407445, 260514, 335777, 537971, 438058, 467405, 455714, 453575, 381289, 292280, 429174, 399366, 346648, 399170, 452970, 393345, 469310, 373569, 371638, 485415, 436931, 466876, 426284, 438970, 314095, 334536, 428323, 414453, 446354, 401898, 419831, 14564, 384680, 455043, 459225, 465595, 293768, 429460, 430424, 416160, 456611, 485504, 444902, 341174, 416051, 484510, 437109, 415312, 435841, 347629, 460648, 469721, 397567, 413594, 374720, 420622, 354285, 437068, 435366, 398175, 400710, 399404, 295693, 396493, 423611, 452557, 473072, 431803, 434203, 324852, 340837, 381283, 141052]
Random movie IDs for the year 2018: [369972, 498248, 425194, 571346, 420426, 531384, 504172, 550776, 505579, 442062, 454227, 385448, 514754, 470918, 440298, 446696, 539649, 496316, 447200, 424783, 297802, 483980, 477489, 550353, 528949, 370567, 518158, 487670, 534338, 439015, 548066, 489939, 396461, 474395, 505262, 522417, 338970, 351286, 417261, 816741, 450001, 524789, 556803, 467956, 455207, 400579, 416494, 299536, 492452, 503616, 535389, 503346, 493551, 456929, 454293, 405774, 371608, 505914, 465136, 487242, 500006, 513347, 412988, 453755, 460555, 484862, 437375, 495193, 510460, 475210, 454652, 454992, 284054, 500475, 507076, 447665, 491584, 412862, 501190, 458594, 401545, 429351, 457136, 552095, 445571, 427641, 440471, 409582, 491472, 505192, 475215, 412546, 375785, 353576, 489929, 434714, 440626, 345887, 432301, 447332, 333339, 500919, 332562, 475888, 385332, 486947, 438145, 532973, 445651, 478820, 348350, 551608, 531593, 351044, 406563, 353081, 458423, 523773, 424694, 426563, 517991, 462919, 515841, 399360, 465914, 401905, 395841, 494656, 337167, 513386, 480530, 433310, 401371, 470229, 369523, 461928, 460885, 513691, 419478, 489763, 448491, 300668, 199928, 527729, 506680, 446021, 396806, 504253, 481203, 420634, 448776, 429197, 487558, 483455, 499028, 538235, 336843, 519091, 543753, 564140, 426814, 458109, 477777, 432527, 523536, 438808, 445456, 407440, 401478, 490132, 512412, 399796, 489925, 494407, 508642, 384521, 493922, 363088, 398173, 387592, 544431, 485942, 402900, 461992, 534780, 437543, 530154, 466532, 283601, 407436, 400535, 385360, 438590, 424121, 467660, 469916, 595801, 495925, 532321, 479226, 457955, 506763, 381719, 454615, 476926, 491418, 339380, 300681, 489931, 426426, 424781, 499546, 492336, 456750, 527435, 418879, 517839, 401200, 406761, 460668, 375588, 449443, 401847, 484247, 502682, 497727, 538858, 521296, 425373, 426865, 454286, 483184, 338952, 434355, 455236, 539517, 360920, 454619, 407439, 529216, 472269, 470878, 549590, 451480, 453201, 446101, 505058, 421658, 424619, 497984, 425505, 400650, 532814, 424139, 426543, 463272, 399174, 400155, 476678, 524738, 429300, 426793, 541611, 530442, 339103, 542905, 414001, 507732, 476299, 268896, 470044, 569547, 375107, 535437, 474354, 467938, 375262, 448095, 542836, 220882, 347866, 566810, 433501, 404368, 434555, 459910, 417812, 526051, 451915, 449176, 536115, 437586, 496328, 454983, 474051, 361292, 443463, 399402, 383498, 473149, 395990, 489999, 391710, 490003, 458737, 420814, 423612, 295011, 411143, 428078, 439079, 455980, 426618, 537190, 545742, 517814, 346910, 426249, 376865, 460399, 429476, 399035, 485811, 353066, 506072, 476968, 419743, 446894, 476292, 502350, 504903, 501902, 530254, 384677, 442249, 523931, 523777, 447743, 335983, 440161, 260513, 567604, 467824, 474392, 426613, 500664, 521190, 452015, 518068, 463821, 564147, 449749, 540468, 554972, 489430, 471474, 347375, 438799, 345940, 407451, 592742, 465109, 528656, 433627, 400090, 480681, 345934, 511785, 497370, 416234, 518963, 523849, 455839, 429415, 507411, 460019, 324857, 437557, 475132, 466282, 449992, 475094, 459928, 401981, 557968, 474335, 463116, 498934, 496704, 477779, 499726, 467632, 464502, 557635, 430040, 446807, 551298, 537996, 401469, 416194]
Random movie IDs for the year 2019: [291867, 565310, 853033, 580600, 428836, 535581, 429617, 512196, 290859, 508763, 567598, 611914, 639838, 554805, 481880, 509967, 615774, 487680, 523139, 553837, 492002, 586954, 534490, 575769, 528091, 480414, 489245, 613099, 574241, 571891, 604605, 545330, 543540, 537055, 373571, 570973, 491283, 582186, 556901, 458897, 535292, 508138, 633604, 1054116, 571627, 420809, 536869, 458305, 519465, 554241, 595985, 586707, 506517, 503919, 431075, 525661, 504608, 505706, 508791, 449459, 574982, 576004, 527776, 620725, 466272, 513514, 458723, 463088, 536743, 502292, 480042, 592350, 514921, 428045, 610892, 560362, 471707, 542417, 617986, 596054, 458131, 338967, 491854, 539892, 551115, 320288, 486589, 522518, 561362, 521777, 566451, 620249, 575094, 560192, 568091, 530081, 452832, 522212, 480434, 447034, 553608, 447404, 560527, 553835, 574074, 614488, 433499, 399579, 501170, 474350, 359724, 499701, 519010, 384018, 411728, 483202, 611014, 369196, 486131, 440762, 524251, 400157, 585268, 540901, 578189, 576071, 587727, 384756, 606117, 510318, 503125, 366668, 570131, 583903, 450465, 515248, 552178, 581387, 567646, 600881, 449924, 480105, 526050, 466081, 431693, 481084, 506528, 570503, 619278, 517116, 538225, 556509, 480001, 549559, 505436, 456740, 473033, 458253, 280217, 574089, 524247, 534259, 554596, 329996, 631143, 569594, 581528, 539651, 652722, 346709, 483906, 517909, 589712, 635744, 515001, 501395, 560066, 551808, 487297, 458156, 532067, 592230, 524780, 398978, 479455, 472674, 533642, 454294, 405177, 586863, 454640, 500916, 502416, 157433, 623627, 504585, 575299, 441282, 181812, 422803, 537056, 413518, 637920, 508664, 421473, 454458, 473019, 461130, 399361, 575222, 500922, 449563, 500682, 517764, 420818, 531309, 417466, 454467, 623464, 638507, 513576, 537140, 572154, 583132, 647498, 704264, 531509, 594188, 301528, 553600, 512895, 393624, 599975, 522938, 640882, 471506, 463257, 514439, 475557, 465003, 586048, 531428, 390634, 571055, 553839, 644613, 606856, 513045, 449985, 522924, 468224, 330457, 605804, 292011, 532671, 531306, 537739, 625450, 615453, 586347, 457799, 504631, 504949, 492188, 463684, 491480, 575813, 401410, 595975, 299537, 530915, 472815, 403300, 573699, 619264, 440472, 512200, 615165, 634524, 607383, 412117, 522892, 623195, 417384, 500852, 820232, 621191, 535167, 614587, 549053, 505600, 445629, 581791, 586940, 460321, 423204, 509730, 559969, 535544, 519473, 506574, 515195, 499338, 585831, 527641, 517024, 530385, 512901, 509874, 727502, 528985, 581531, 546554, 527261, 528888, 331482, 505948, 496243, 587272, 299534, 623520, 522162, 482981, 514999, 463843, 537061, 449562, 611468, 533444, 612152, 501907, 539531, 525657, 529983, 484468, 511322, 512263, 612654, 530079, 568160, 606154, 508965, 465086, 511987, 431580, 614722, 520901, 433502, 622420, 554600, 532812, 508101, 576393, 574883, 532953, 521029, 609734, 295151, 419704, 420817, 591278, 548473, 551332, 416153, 638134, 595172, 517093, 537915, 531503, 582927, 166428, 416144, 530382, 658009, 572468, 438674, 484641, 522681, 459992, 509585, 302349, 581997, 453405, 628707, 567609, 565743, 287947, 433249, 487616, 535845, 505953, 661289, 625976, 327331, 449684, 438650, 538207, 567006, 547404, 611207]
Random movie IDs for the year 2020: [581577, 529485, 647325, 589174, 576156, 652837, 759923, 753583, 524216, 602301, 590706, 589615, 500840, 338762, 740903, 606234, 682110, 737568, 571384, 514593, 722261, 520765, 623856, 480857, 576920, 560044, 577922, 614626, 539885, 575604, 571625, 581526, 805627, 668195, 550231, 385103, 643550, 608195, 595671, 651571, 592834, 618353, 635389, 563067, 579831, 635918, 594328, 508935, 631132, 802119, 602296, 680028, 516486, 678489, 627290, 703771, 337401, 531499, 721656, 684684, 602211, 628333, 686245, 660982, 520318, 647785, 724106, 664280, 556984, 529203, 531299, 787428, 454626, 522627, 688258, 505379, 688335, 682153, 637969, 603119, 723072, 726208, 520725, 609242, 635199, 690846, 624963, 502033, 733317, 703134, 687156, 677115, 560144, 606679, 615115, 653598, 567965, 441130, 522466, 528085, 602147, 532870, 571648, 624808, 741074, 497582, 621870, 575417, 663870, 516632, 475430, 716703, 599521, 761563, 443791, 664767, 604822, 670428, 614911, 38700, 651589, 627725, 690369, 601844, 492008, 509635, 430155, 768520, 693158, 656690, 728754, 636565, 717634, 628917, 738215, 508439, 320846, 595148, 616251, 523371, 592656, 596161, 553604, 677638, 702525, 559974, 342470, 729097, 520172, 743601, 533514, 740996, 757860, 618355, 653567, 670266, 635302, 585244, 505746, 446893, 570670, 558582, 606523, 707886, 653744, 492611, 853315, 575776, 689723, 597219, 664031, 626393, 671583, 637157, 594530, 725273, 595149, 661914, 682589, 696374, 549514, 654028, 571265, 746817, 724717, 448119, 582596, 740985, 600354, 601165, 708336, 616803, 710692, 555974, 522444, 615667, 703745, 390635, 617786, 660330, 641662, 552532, 571785, 718444, 680593, 659986, 611213, 741011, 573730, 514207, 651610, 605116, 674944, 618354, 581600, 642121, 602654, 442065, 660713, 749618, 556574, 651070, 586461, 721452, 738646, 632632, 702936, 656563, 592984, 622855, 517412, 625568, 770156, 621013, 701175, 501979, 716263, 594718, 524047, 542224, 618344, 715123, 658751, 701121, 653610, 517096, 435615, 680041, 346687, 514847, 521034, 429422, 582014, 698860, 531876, 612706, 736759, 659991, 513268, 547016, 615677, 549294, 489326, 613504, 587792, 583083, 545609, 581032, 667520, 635237, 759902, 653664, 454433, 743904, 658777, 425001, 662708, 733156, 665760, 665251, 713776, 526019, 590223, 674607, 592279, 546121, 633515, 552269, 479259, 575088, 340102, 474764, 526702, 527660, 632322, 658760, 466622, 513584, 526007, 587562, 664413, 604578, 565426, 755812, 539537, 568467, 689249, 520946, 668742, 508442, 718867, 705862, 505225, 774372, 652962, 575291, 716258, 706503, 743439, 719643, 499932, 621151, 581392, 507441, 665139, 724108, 539181, 581859, 590995, 495764, 720395, 719410, 710356, 508570, 718840, 606625, 728142, 531454, 673174, 639798, 580175, 710217, 339095, 614696, 720755, 659959, 447362, 604362, 551804, 707610, 457335, 730497, 579583, 556678, 589754, 662546, 458576, 692969, 627463, 400160, 644831, 614560, 481848, 619592, 615665, 587496, 641501, 567608, 655431, 623491, 522241, 666750, 634521, 643882, 464052, 502425, 721625, 715287, 718400, 655187, 617505, 628866, 715756, 724475, 653615, 579828, 531219, 560050, 675327, 630566, 491926, 661950, 623305, 523781, 523977, 522406, 681019, 574396, 761053, 575774]
Random movie IDs for the year 2021: [758866, 654754, 786705, 711963, 617502, 664574, 715874, 591274, 529106, 646380, 542178, 801058, 853588, 501841, 761898, 739990, 628534, 873972, 630004, 581734, 619297, 763788, 660942, 726684, 554594, 877183, 615457, 825997, 845222, 524434, 770254, 581644, 730823, 412656, 516329, 631843, 602666, 811592, 804755, 797271, 760883, 843906, 871964, 578701, 693827, 826749, 654739, 691179, 520763, 671043, 660120, 614409, 595813, 451048, 423108, 547565, 630392, 864873, 763164, 681887, 567189, 843241, 615666, 813258, 544401, 791373, 649394, 630586, 438695, 845861, 672582, 706872, 593395, 619778, 876716, 630240, 773736, 662237, 667276, 458220, 874300, 484718, 425909, 730840, 662638, 753601, 796849, 774343, 471498, 628914, 764835, 449406, 525660, 805195, 802443, 785976, 918692, 639721, 672741, 718032, 347626, 736074, 592863, 852370, 653349, 850099, 504148, 559581, 676705, 579047, 809314, 586791, 524369, 841755, 762079, 592508, 611698, 760336, 763329, 385128, 775244, 834404, 788929, 875104, 732450, 602302, 710871, 745881, 568124, 749274, 775996, 881460, 567748, 583406, 920081, 753453, 801335, 850818, 804435, 817485, 508943, 587996, 645886, 787723, 595743, 597316, 785533, 585245, 526811, 399566, 613335, 583689, 695958, 866038, 791469, 581389, 664236, 614917, 587807, 846214, 797787, 576925, 716594, 565028, 643586, 763148, 513692, 675445, 672647, 663260, 893694, 682587, 769749, 717192, 683311, 527774, 698201, 796499, 534072, 644495, 567797, 779047, 615658, 585216, 609972, 512025, 550988, 810693, 797838, 805411, 597208, 599025, 672437, 856245, 606236, 823610, 576784, 647581, 602223, 870096, 842033, 557950, 600583, 776503, 768726, 760873, 811050, 800497, 617762, 960481, 645710, 663558, 797394, 588921, 774741, 729679, 506281, 283566, 379686, 820530, 831405, 580489, 523638, 744746, 685274, 896221, 632357, 920342, 920258, 740658, 513310, 583833, 637534, 808023, 497698, 522931, 617653, 646207, 798544, 618851, 436969, 603661, 591275, 811634, 740925, 615643, 638449, 598331, 736545, 763152, 607259, 773543, 580532, 870404, 460465, 865921, 624789, 645861, 576845, 634649, 811367, 512195, 763025, 633802, 520663, 727745, 607844, 568620, 680531, 803279, 690957, 821792, 851925, 454527, 767304, 744275, 780382, 438631, 716612, 810271, 877013, 597891, 559907, 573680, 511809, 895001, 793998, 573164, 566525, 672322, 666243, 598896, 644583, 785545, 695932, 817451, 760195, 734796, 602269, 532865, 602063, 869250, 669671, 628704, 768744, 522402, 726429, 823609, 597890, 482321, 566076, 589761, 699102, 634528, 654974, 625169, 726916, 567690, 370172, 729720, 574060, 645689, 750253, 645788, 860867, 790523, 795514, 865463, 629017, 773621, 537116, 842171, 776797, 777270, 604360, 460458, 785539, 866678, 592695, 550205, 459151, 410113, 738652, 862855, 792678, 522478, 637649, 613911, 714869, 591538, 860623, 798141, 683127, 629028, 588228, 776305, 807196, 482373, 851976, 337404, 795607, 675319, 581726, 593910, 503736, 649409, 673309, 586047, 623135, 779029, 722913, 623511, 765245, 610253, 797946, 618162, 831223, 638597, 791568, 637693, 579741, 602734, 785663, 736073, 501929, 476669, 693113, 766798, 539565, 650031, 644092, 789708, 624860, 834143, 601666, 591273, 736069, 768147, 616651, 728118]
Random movie IDs for the year 2022: [541134, 779782, 924482, 803114, 869626, 791155, 805327, 667739, 335795, 615777, 760104, 928270, 818110, 864101, 818647, 1013860, 949423, 396194, 818397, 877957, 901908, 642885, 942890, 800815, 995133, 1041513, 436270, 911129, 724665, 753232, 990691, 717728, 844398, 606403, 969686, 406759, 587412, 705861, 536554, 773867, 1001865, 921360, 573171, 718930, 938008, 601796, 965143, 760204, 948276, 338953, 676701, 833339, 942881, 828613, 539681, 335787, 858485, 1022128, 1015724, 1028725, 697799, 632856, 993867, 740952, 756999, 842942, 610363, 632727, 890656, 675054, 820912, 888195, 628900, 979924, 1004663, 989937, 1028703, 945675, 852046, 1032950, 1033219, 751237, 504827, 976906, 783675, 999278, 639933, 799155, 49046, 951546, 766507, 615904, 759175, 788752, 634429, 1022256, 844075, 689700, 939210, 751741, 780609, 758009, 801913, 843847, 755566, 829557, 758724, 646385, 880841, 976720, 935271, 508947, 1027604, 829799, 1003813, 809140, 1020969, 840326, 817758, 744594, 774825, 616820, 949567, 555604, 900667, 668482, 916605, 782054, 698948, 899082, 732459, 792358, 1007401, 987750, 965571, 507086, 942576, 727340, 883502, 916224, 980083, 862551, 960704, 965150, 863929, 615173, 801965, 683361, 937013, 1036956, 799876, 931599, 974961, 414906, 664469, 628878, 1010823, 1024627, 836009, 760741, 785084, 985932, 882598, 675353, 698508, 864370, 723419, 663712, 926899, 505026, 606870, 1026563, 585083, 1015936, 760161, 338947, 862965, 579974, 520023, 532639, 629542, 852448, 668461, 886396, 801071, 591120, 968739, 597845, 1018494, 893228, 762975, 245842, 338958, 893712, 790142, 758330, 829410, 800345, 800510, 916740, 843380, 732684, 1011679, 526896, 807356, 664996, 1132079, 803104, 820446, 790525, 626872, 619730, 653851, 683328, 736526, 876792, 860159, 760868, 830788, 557946, 934194, 799379, 939899, 760099, 876797, 818750, 545611, 1005776, 765172, 505642, 795774, 736732, 804413, 619979, 294793, 1041317, 1024530, 879444, 1037858, 778810, 848123, 961651, 913290, 585511, 894205, 877269, 760926, 315162, 791177, 366672, 880313, 829280, 76600, 893370, 960258, 836225, 1020696, 791348, 903939, 816977, 588108, 804095, 1006540, 962232, 1015606, 766475, 897192, 899112, 1010818, 826680, 997120, 631947, 955971, 890980, 453395, 1045944, 966220, 709716, 937278, 725201, 810171, 843932, 820067, 787752, 833593, 885303, 420821, 610150, 1036561, 674324, 656663, 575322, 614939, 913205, 1001835, 560057, 696157, 1010821, 1025169, 616037, 862491, 624091, 947890, 931405, 752623, 777831, 614934, 661374, 730167, 983507, 457232, 1058732, 806368, 1018403, 800937, 979296, 965731, 892153, 361743, 901563, 828853, 942047, 629176, 718789, 619803, 438148, 763285, 497828, 615952, 774752, 829560, 1059673, 854239, 851644, 821937, 551271, 873126, 916192, 301502, 928123, 872542, 429473, 817648, 597922, 809107, 814338, 517302, 743563, 744276, 683340, 648579, 874745, 1034337, 1017794, 626735, 929170, 1015602, 985939, 896485, 522016, 715931, 762968, 705996, 1043565, 938567, 864116, 588555, 1049233, 760517, 961484, 936621, 696806, 661231, 518896, 879957, 746333, 894169, 811631, 833425, 644124, 615469, 754452, 606402, 682075, 724495, 800939, 846433, 823625, 890322, 762504, 995906, 556694, 593643, 676547, 899294, 682507, 831946, 830784, 955991, 960139]
Random movie IDs for the year 2023: [1189205, 1029575, 940721, 1016121, 943134, 964592, 740441, 643215, 1085103, 882569, 1070514, 1081291, 638974, 868759, 862557, 1142414, 1016084, 838240, 1073125, 928833, 1059372, 353577, 983044, 893345, 813477, 1172676, 850165, 594767, 1134055, 1059264, 1117609, 1120628, 994143, 899445, 906221, 958263, 1026227, 768362, 955531, 572802, 697843, 1003581, 1074034, 916433, 808396, 1181678, 1024621, 1001884, 864168, 1025491, 729854, 943930, 1139087, 646389, 516806, 726209, 942922, 1163258, 664341, 1006462, 1171541, 1015303, 1172674, 586810, 848187, 845387, 1098160, 814776, 770906, 862552, 975022, 269509, 1118772, 832502, 1040148, 798021, 296271, 1062807, 805320, 985617, 1056380, 820609, 1097150, 1134433, 299054, 420808, 966719, 1100795, 1183905, 1003579, 1034940, 523607, 346698, 897160, 552688, 798286, 1002185, 1008005, 1081676, 912916, 970284, 974931, 1006911, 857553, 1002338, 1140168, 937249, 1072790, 998022, 976893, 1151534, 1010826, 555285, 1103825, 951491, 1057001, 1008042, 812225, 945729, 1179496, 677179, 1020394, 961268, 1143190, 998731, 945937, 1056803, 891699, 968051, 1122504, 1024773, 1159518, 1039690, 1188104, 975902, 700391, 1075335, 744857, 1010928, 467244, 447365, 852432, 1214667, 912908, 678512, 856289, 1174618, 1150394, 616747, 842675, 1083862, 507532, 1032948, 1003580, 1088563, 1147400, 605886, 859235, 800158, 1027073, 926393, 1134754, 575264, 695721, 1056360, 934433, 1046032, 1152624, 1154864, 1058694, 1094844, 807172, 948713, 991708, 866346, 988078, 956964, 842544, 1050035, 958196, 930564, 964980, 1075175, 1035982, 950526, 840430, 640146, 1123093, 982940, 939335, 615656, 739405, 897087, 1010581, 898673, 614479, 520758, 949429, 709631, 457332, 620705, 1071215, 790493, 1039960, 1079394, 843846, 845783, 713704, 617882, 1143319, 1136318, 979275, 1138749, 967582, 854887, 1046090, 1179558, 982932, 1211483, 990140, 921636, 999644, 729165, 758323, 635910, 898713, 779816, 753342, 872585, 848326, 944401, 996154, 1205781, 849869, 1160003, 1156125, 958006, 812037, 1022796, 855263, 496450, 1020006, 877703, 872906, 880100, 508883, 335977, 1087040, 1161048, 507089, 298618, 1154598, 1111966, 890771, 736790, 837335, 884605, 899579, 943344, 455476, 984249, 915935, 1109237, 912480, 866463, 532408, 603692, 1061181, 762430, 776835, 804150, 1064835, 800787, 1140066, 1063879, 667538, 820525, 447277, 626332, 747188, 845659, 649609, 1085046, 927107, 724209, 959092, 734253, 979097, 1106904, 631842, 1086591, 459003, 995012, 1049948, 1077280, 783110, 930094, 870518, 1078012, 1051560, 1076487, 1075334, 717930, 824003, 1076364, 1145491, 913673, 1070802, 609681, 1122932, 1017066, 845111, 1064178, 1008102, 816904, 976573, 1047041, 994108, 1107387, 569094, 852436, 650036, 792293, 796185, 830764, 466420, 666277, 1146302, 906126, 670292, 1149381, 365620, 1053592, 1145028, 1053544, 980078, 565770, 1098110, 961323, 842945, 781732, 980489, 937746, 614930, 1134424, 1115396, 955555, 1190012, 966576, 502356, 493529, 1144962, 997265, 734002, 1186957, 852445, 931102, 787699, 839369, 726759, 1173558, 893723, 1075794, 823482, 1102776, 944117, 1034062, 1071806, 980026, 558915, 976830, 1121612, 870358, 792307, 878361, 717980, 940551, 862968, 385687, 667717, 760774, 1127227, 1085218, 986070, 660360, 889675, 745391, 667216, 1024721, 984105, 536437, 1024127, 901362, 964960, 1016346, 960033, 923939, 1060090, 1057491, 1160164]
Random movie IDs for the year 2024: [1059128, 1049574, 1241674, 1225377, 974576, 829402, 1129598, 1022789, 1239251, 984324, 1291559, 646097, 1228681, 1214509, 1088514, 1244492, 1284004, 1118028, 1326059, 1101448, 1087388, 1186947, 866398, 826510, 967847, 1147416, 917496, 948549, 863873, 1025596, 748783, 1260594, 402431, 857598, 974453, 698687, 636279, 1114738, 646683, 1014505, 1128606, 507241, 516729, 1148713, 1182387, 835113, 1028196, 827931, 1207898, 1182047, 1184918, 1115623, 974950, 1215162, 1354627, 1171462, 1387198, 1264429, 1299652, 1084863, 1263421, 1333100, 823464, 1010161, 592831, 974036, 1013154, 974635, 1241982, 975511, 1215185, 799583, 647245, 1408248, 539972, 634492, 932086, 1249013, 1147710, 1124641, 804616, 936245, 1180629, 1174481, 1026436, 991610, 718821, 1079091, 1387866, 1397347, 726139, 845781, 1399914, 616446, 839033, 1139106, 1032823, 840705, 932420, 1094274, 1023922, 970347, 560016, 1103621, 801688, 934632, 1207123, 970450, 558449, 938614, 956842, 1059064, 1291594, 1309923, 929590, 413846, 720321, 1372665, 1114513, 1154215, 1002088, 1001274, 1190868, 976576, 1235623, 1280768, 1328814, 1373893, 1278263, 1084736, 1186532, 1128941, 791042, 1151949, 995803, 1239146, 1075676, 1214484, 1109255, 989662, 1165466, 1104844, 1226578, 1238358, 1077782, 1154304, 1311550, 533535, 957452, 1000075, 1331344, 1005076, 639720, 1072342, 1247597, 1127166, 978796, 1011985, 1159477, 1008953, 1047373, 1281826, 1186350, 359410, 914206, 1033462, 653346, 976734, 1097549, 636706, 1216191, 1156593, 1102493, 1038263, 1118031, 717196, 1097870, 940139, 1075789, 1209217, 1257400, 704673, 1082195, 1371727, 1016848, 1130053, 1278099, 1061990, 1208808, 728949, 969492, 1212073, 573435, 748167, 1023915, 1100782, 799766, 1043905, 1123278, 1211472, 972614, 1276210, 832964, 1096197, 1014661, 843527, 598387, 1122418, 811941, 1017163, 1141182, 1139829, 937287, 1287717, 1086747, 1359227, 1208491, 1221404, 947891, 987686, 1005331, 1209288, 1100856, 1010600, 923667, 949484, 1252309, 848538, 746036, 1355755, 1081012, 1252428, 1340602, 1357633, 1242372, 870360, 426063, 5492, 1094974, 1116490, 945961, 1329912, 1299372, 1171640, 1142518, 1064375, 614933, 1125311, 1262983, 1111873, 955916, 889737, 1241752, 1232827, 931944, 1358752, 1035048, 719221, 1159311, 1006724, 959098, 1041613, 673593, 1120911, 1331375, 933260, 1163194, 661539, 1037113, 1389147, 930600, 1051891, 762509, 1388694, 1207830, 1140648, 1222248, 939243, 592983, 1308546, 1252470, 785542, 1155095, 739547, 365177, 896151, 929204, 1247019, 1034541, 1139566, 786892, 1192209, 693134, 1329336, 933131, 1093995, 1012201, 814889, 1155089, 549509, 1063877, 1181863, 1042657, 1029281, 1094556, 1061699, 882059, 1128650, 1160956, 1025463, 1079485, 1029235, 1154223, 957304, 1051896, 1008409, 1306764, 1001311, 974262, 1232449, 645757, 1308757, 1241320, 1105407, 1217690, 519182, 763215, 280180, 1361292, 1014590, 1062644, 1094138, 1290938, 1185719, 1125510, 838209, 959604, 1210794, 1048241, 1249532, 1052280, 912649, 1296753, 395817, 1087421, 1062215, 1091267, 1000837, 929563, 1241748, 1064486, 1317218, 1300962, 1235499, 1337309, 1029955, 1064213, 974250, 1180634, 1020896, 1013850, 662402, 1026999, 1160018, 807339, 588648, 641934, 605722, 1091181, 567811, 1276945, 1222064, 978592, 927342, 1139817, 858017, 831815, 1389038, 1087822, 748230, 914215, 1255208, 1278101, 626412, 1066262, 1100099, 639857, 1196470, 1167271, 1089123, 869291, 762441, 823219, 618588, 704239, 437342, 1029330, 928480, 1106739, 877817]

1.1.2 Request movie names based on IDs

import requests
import pandas as pd

API_KEY = '436b4ebe719b3f41c64f720254718b77'  # Replace with your TMDb API key

# Read movie IDs from CSV file
ids_df = pd.read_csv('/Users/heshuqing0305/Downloads/movie_all_ids_2014_2024_tmdb.csv')
movie_ids = ids_df['Movie ID'].tolist()  # Get the list of movie IDs

movie_titles = []

for movie_id in movie_ids:
    url = f'https://api.themoviedb.org/3/movie/{movie_id}'
    params = {
        'api_key': API_KEY,
        'language': 'en'  # Set language to English
    }

    response = requests.get(url, params=params)
    data = response.json()

    if response.status_code == 200:
        movie_titles.append(data['title'])  # Get movie title
    else:
        print(f"Unable to retrieve movie information for ID {movie_id}: {data.get('status_message', 'Unknown error')}")

# Print movie titles
print("Retrieved movie titles:")
for title in movie_titles:
    print(title)

# Save movie titles to CSV file
titles_df = pd.DataFrame(movie_titles, columns=['Movie Title'])
titles_df.to_csv('movie_all_titles_2014_2024_tmdb.csv', index=False, encoding='utf-8-sig')

Retrieved movie titles:
X-Men: Days of Future Past
If I Stay
Leviathan
The Purge: Anarchy
Adult World
Batman: Assault on Arkham
Get on Up
Doraemon: New Nobita's Great Demon - Peko and the Exploration Party of Five
Godzilla
Gun Woman
The Town that Dreaded Sundown
Dolphin Tale 2
What We Did on Our Holiday
Transcendence
Zombie Fight Club
Kill the Messenger
The Salvation
Birdman or (The Unexpected Virtue of Ignorance)
The Captive
Alexander and the Terrible, Horrible, No Good, Very Bad Day
Beyond the Reach
Whiplash
About Last Night
The Boxtrolls
Before I Go to Sleep
That Awkward Moment
Spring
Jacky in the Kingdom of Women
A Pigeon Sat on a Branch Reflecting on Existence
Skin Trade
The Interview
The Lego Movie
Scooby-Doo! WrestleMania Mystery
The Other Woman
Pride
The Quiet Ones
Calvary
Two Night Stand
I, Frankenstein
[REC]⁴ Apocalypse
Road to Paloma
The Snow Queen 2: Refreeze
Alien Abduction
In Your Eyes
Guardians of the Galaxy
Indigenous
The Imitation Game
I Origins
The Remaining
Still Alice
A Long Way Down
Teenage Mutant Ninja Turtles
The Normal Heart
Messi
Lupin the 3rd
Joe
The Third One
Night Moves
Stand by Me Doraemon
The Signal
Love, Rosie
Maleficent
Hercules
Annabelle
Million Dollar Arm
Selma
Chef
Dawn of the Planet of the Apes
Blue Ruin
Halo: Nightfall
When the Game Stands Tall
Dead Snow 2: Red vs. Dead
No Tears for the Dead
Deliver Us from Evil
Sub Rosa
Wild
A Girl Walks Home Alone at Night
The Water Diviner
Annie
Where Hope Grows
Big Hero 6
Barbie: The Pearl Princess
The Fault in Our Stars
Maya the Bee Movie
Shrew's Nest
Phoenix
Babysitting
The One I Love
Night at the Museum: Secret of the Tomb
Seal Team Eight: Behind Enemy Lines
Top Five
The Torture Club
Dragon Nest: Warriors' Dawn
Exodus: Gods and Kings
300: Rise of an Empire
Hector and the Search for Happiness
The November Man
Yves Saint Laurent
Third Person
Crayon Shin-chan: Intense Battle! Robo Dad Strikes Back
American Sniper
How to Train Your Dragon 2
Jai Ho
Locke
3 Days to Kill
The Hunger Games: Mockingjay - Part 1
'71
Detective Conan: Dimensional Sniper
Starry Eyes
Divergent
Effie Gray
Northmen: A Viking Saga
Sin City: A Dame to Kill For
Black Sea
The Expendables 3
Mr. Peabody & Sherman
The Connection
Taken 3
Song of the Sea
The Little Death
Paranormal Activity: The Marked Ones
The Bélier Family
In the Blood
Ride
Pompeii
Rise of the Legend
Monster High: Freaky Fusion
The Frame
Behaving Badly
Haunt
Brick Mansions
The Dead Lands
Enemy
Under the Skin
The Monkey King
The Hobbit: The Battle of the Five Armies
Edge of Tomorrow
Ask Me Anything
For a Handful of Kisses
The Pirates
The Admiral: Roaring Currents
Nightcrawler
GirlHouse
Jessabelle
The Angriest Man in Brooklyn
Into the Woods
You're Not You
Petals on the Wind
A Girl at My Door
Flowers in the Attic
What We Do in the Shadows
The Amazing Spider-Man 2
22 Jump Street
The Drop
Tamako Love Story
The White Haired Witch of Lunar Kingdom
Creep
Son of God
Serial (Bad) Weddings
Trash
No Good Deed
Camp X-Ray
Endless Love
E N V O Y
Iceman
Moms' Night Out
The Gambler
The Science of Interstellar
Draft Day
Justice League: War
Planes: Fire & Rescue
The Rewrite
Inherent Vice
The Judge
Tazza: The Hidden Card
Marvel Studios: Assembling a Universe
Automata
The Perfect Dictatorship
Tinker Bell and the Pirate Fairy
The Prince
A Million Ways to Die in the West
Walking on Sunshine
The Invisible Boy
Beyond the Lights
Tom and Jerry: The Lost Dragon
Parasyte: Part 1
Labyrinth of Lies
Kundo: Age of the Rampant
Born to Race: Fast Track
Vampire Academy
A Merry Friggin' Christmas
American Heist
Begin Again
Two Days, One Night
Iron Man & Captain America: Heroes United
Muppets Most Wanted
The Legend of Hercules
The Way He Looks
Let's Be Cops
The Equalizer
Avengers Confidential: Black Widow & Punisher
Miss Meadows
Seventh Son
Transformers: Age of Extinction
In Darkness We Fall
The Voices
Tammy
Flower & Snake: Zero
The Forger
Toy Story That Time Forgot
Before We Go
SlugTerra: Return of the Elementals
The Giver
Young Mother 2
Scarlet Innocence
Interstellar: Nolan's Odyssey
PK
Boys
Spanish Affair
Rape Zombie: Lust of the Dead 5
Samba
The Woman in Black 2: Angel of Death
Dumb and Dumber To
No Thank You
The Book of Life
Calculator
Jack and the Cuckoo-Clock Heart
The Duke of Burgundy
Boyhood
Frontera
Anatomy of a Love Seen
As the Gods Will
Testament of Youth
Ghost in the Shell: Arise - Border 3: Ghost Tears
Alien Outpost
A Haunted House 2
Kristy
Love Affair
Good People
Everly
Beauty and the Beast
Wild Tales
The Rover
The Monuments Men
V/H/S: Viral
Zapped
Recently, My Sister is Unusual
The Nut Job
The Babadook
Eden
Ouija
Dragons: Dawn of the Dragon Racers
Electric Boogaloo: The Wild, Untold Story of Cannon Films
Noah
Winter's Tale
Penguins of Madagascar
Unfriended
Swindle
The Good Lie
The Theory of Everything
Obsessed
A Most Violent Year
Rio 2
Burying the Ex
Falcon Rising
Outcast
Sorry If I Call You Love
Sharktopus vs. Pteracuda
Asterix: The Mansions of the Gods
Kite
Into the Storm
Joy Ride 3
Monster High: Frights, Camera, Action!
Veronica Mars
Wolves
Zombeavers
Lava
Flight 7500
The Hundred-Foot Journey
Happy New Year
Jack Ryan: Shadow Recruit
Rurouni Kenshin Part II: Kyoto Inferno
Horrible Bosses 2
Heaven Is for Real
The Maze Runner
Kung Fu Jungle
Last Shift
The Absent One
Laggies
Son of Batman
10.0 Earthquake
Need for Speed
Naked Ambition 3D
Kaththi
Reach Me
Addicted
Neighbors
St. Vincent
Left Behind
Mommy
Dark Was the Night
Date and Switch
As Above, So Below
The Last: Naruto the Movie
Tusk
The Secret Sex Life of a Single Mom
John Wick
Cabin Fever: Patient Zero
Queen
Black Butler: Book of Murder
Robot Overlords
Big Eyes
Barefoot
White Bird in a Blizzard
Miss Granny
Leprechaun: Origins
When Marnie Was There
Marshland
The Guest
God's Not Dead
Step Up All In
Ode to My Father
The Grand Budapest Hotel
Clouds of Sils Maria
The Assault
The Loft
My Mistress
In the Name of the King III
Blended
Cake
My Little Pony: Equestria Girls - Rainbow Rocks
This Is Where I Leave You
Son of a Gun
Wrong Turn 6: Last Resort
Deadly Virtues: Love. Honour. Obey.
Dracula Untold
13 Sins
A Hard Day
The Legend of the Guanajuato Mummies
Playing It Cool
RoboCop
Pokémon the Movie: Diancie and the Cocoon of Destruction
Lucy
And So It Goes
The Raid 2
Sabotage
Rampage: Capital Punishment
Zodiac
The Tribe
One Piece "3D2Y": Overcome Ace's Death! Luffy's Vow to his Friends
Ride Along
Boy Meets Girl
Saint Seiya: Legend of Sanctuary
The Riot Club
Fury
For the Emperor
Interstellar
Think Like a Man Too
The Pyramid
Devil's Due
The Cobbler
The Possession of Michael King
Paddington
Serena
Main Tera Hero
Rurouni Kenshin Part III: The Legend Ends
Sapphire Blue
A Most Wanted Man
Cloud 9
Cooties
Who Am I
Rage
Non-Stop
Sex Tape
Citizenfour
Grace
Tinker Bell and the Legend of the NeverBeast
Marvel One-Shot: All Hail the King
Escobar: Paradise Lost
Unbroken
The Little Rascals Save the Day
Premature
Clown
Phineas and Ferb: Star Wars
Maps to the Stars
Life After Beth
Frank
A Walk Among the Tombstones
Predestination
Stonehearst Asylum
Faults
The Best of Me
Mortadelo and Filemon: Mission Implausible
Gone Girl
Rudderless
The Taking of Deborah Logan
Foxcatcher
Walk of Shame
The Homesman
Captain America: The Winter Soldier
My Friend's Wife
The Corpse of Anna Fritz
Krampus
Hot Tub Time Machine 2
Doraemon: Nobita and the Space Heroes
Concussion
The End of the Tour
Unfinished Business
Attack on Titan II: End of the World
Riot
Wild Card
Avengers Grimm
Mad Max: Fury Road
Sleeping with Other People
The Wave
Anomalisa
Freaks of Nature
Beasts of No Nation
Zipper
The Walk
Our Times
Lumberjack Man
Bravetown
400 Days
Heidi
Pan
High-Rise
Circle
Sicario
Batman vs. Robin
Kingsman: The Secret Service
Aloha
Female War: A Nasty Deal
Regression
Synchronicity
The 33
Hot Girls Wanted
The Danish Girl
Youth
War Room
Parasyte: Part 2
Crimson Peak
The Human Centipede 3 (Final Sequence)
Macbeth
Danny Collins
Superfast!
Miss Hokusai
Solace
Invisible Sister
Absolutely Anything
Daddy's Home
Project Almanac
Terminator Genisys
Mom's Friend
Return to Sender
Final Girl
Pawn Sacrifice
Goodnight Mommy
Hardcore Henry
Magic Mike XXL
Ronaldo
Inside Men
Home Sweet Hell
The Flintstones and WWE: Stone Age SmackDown!
Empire of Lust
Amy
Spy
Pay the Ghost
Run All Night
Creed
Forsaken
The Anthem of the Heart
The Miracle
By the Sea
Our Brand Is Crisis
Mortdecai
Last Knights
Regular Show: The Movie
The Dressmaker
Colonia
Hitman: Agent 47
Man Down
The Visit
The Beauty Inside
Mistress America
Full Out
Attack on Titan
The Vatican Tapes
Homesick
The Assassin
Cinderella
Hell House LLC
I Spit on Your Grave III: Vengeance Is Mine
Spotlight
The Stanford Prison Experiment
Victoria
The Martian
Southpaw
The Ridiculous 6
Fantastic Four
Home
Knock Knock
Remember
Avengers: Age of Ultron
Slow West
Dark Places
Maggie
Pixels
Bad Hair Day
Tracers
Goosebumps
Truth
Jurassic World
LEGO DC Comics Super Heroes: Justice League vs. Bizarro League
A Little Chaos
Woman in Gold
Air
The SpongeBob Movie: Sponge Out of Water
The Last Witch Hunter
Walt Before Mickey
Trumbo
Miss You Already
Bāhubali: The Beginning
Wolf Warrior
The Intern
99 Homes
Freeheld
The Tiger
Hotel Transylvania 2
Self/less
Pokémon the Movie: Hoopa and the Clash of Ages
Always Watching: A Marble Hornets Story
American Ultra
Look Who's Back
Legend
Memories of the Sword
Child 44
The Virgin Psychics
The Boy Next Door
Survivor
Brooklyn
Gangnam Blues
The Lazarus Effect
LEGO Marvel Super Heroes: Avengers Reassembled!
The Diary of a Teenage Girl
Monkey King: Hero Is Back
Purpose of Reunion
The Perfect Guy
Eye in the Sky
Inside Out
Equals
Son of Saul
Trainwreck
Me and Earl and the Dying Girl
Scouts Guide to the Zombie Apocalypse
Our Little Sister
Savageland
Manglehorn
Hot Pursuit
Battle for Sevastopol
Scooby-Doo! and Kiss: Rock and Roll Mystery
Irrational Man
Chappie
The Priests
Frankenstein
Meadowland
Abandoned
The Gift
Paranormal Activity: The Ghost Dimension
Boruto: Naruto the Movie
Piku
Southbound
The Final Master
Spectre
Entourage
Momentum
Spooks: The Greater Good
Focus
The Second Mother
She's Funny That Way
In the Heart of the Sea
The Escort
Land of Mine
Room
The Hunger Games: Mockingjay - Part 2
Jesse Stone: Lost in Paradise
Swung
Digimon Adventure tri. Part 1: Reunion
Mutual Relations
Dragon Blade
Stalked by My Doctor
True Story
Intruders
Even Lambs Have Teeth
Baby
San Andreas Quake
Experimenter
Close Range
90 Minutes in Heaven
Max
The Opposite Sex
Suffragette
Mission: Impossible - Rogue Nation
Paul Blart: Mall Cop 2
One Wild Moment
Insidious: Chapter 3
Bone Tomahawk
Hidden
Absolution
Suburra
The Boy and the Beast
Deep Web
Code Geass: Akito the Exiled 4: Memories of Hatred
Monster High: Boo York, Boo York
Woodlawn
The Gallows
I Am Dragon
The Gunman
The Night Before
Heist
The New Adventures of Aladdin
The Peanuts Movie
Tremors 5: Bloodlines
Ip Man 3
Bajrangi Bhaijaan
Ghost in the Shell: The New Movie
Demonic
My Little Pony: Equestria Girls - Friendship Games
McFarland, USA
The Lure
Straight Outta Compton
The Night Crew
The Gigolo
Victor Frankenstein
Stormageddon
The Lobster
Ooops! Noah Is Gone...
Antigang
Fathers and Daughters
Hello, My Name Is Doris
Backcountry
The Hallow
The Good Dinosaur
The Brand New Testament
Jane Got a Gun
Justice League: Throne of Atlantis
Pitch Perfect 2
Suite Française
Animal Kingdom: Let's Go Ape
Obsession
Mojin: The Lost Legend
The Runner
Ant-Man
Barbie & Her Sisters in the Great Puppy Adventure
Monk Comes Down the Mountain
Knight of Cups
Windstorm 2
Tomorrowland
Riley's First Date?
Into the Grizzly Maze
The Survivalist
Burnt
Detective Chinatown
Assassination Classroom
Turbo Kid
Spanish Affair 2
Dragon Ball Z: Resurrection 'F'
Star Wars: The Force Awakens
Barbie in Rock 'N Royals
Cleveland Abduction
Sinister 2
Ted 2
The Marine 4: Moving Target
One Piece: Adventure of Nebulandia
Vacation
Justice League: Gods and Monsters
Casa Amor: Exclusive for Ladies
The Condemned 2
The Clan
The Man from U.N.C.L.E.
Lake Placid vs. Anaconda
A Walk in the Woods
Tokyo Ghoul: Jack
San Andreas
12 Rounds 3: Lockdown
Queen of the Desert
The Age of Adaline
Strange Magic
Love
Big Game
Descendants
Monster Hunt
Made in France
Good Kill
The Man with the Iron Fists 2
It Follows
We Are Your Friends
Carol
Detective Conan: Sunflowers of Inferno
The Sand
The DUFF
Some Kind of Beautiful
Veteran
Bad Sister
Assassination
The Student
Masaan
Dragonheart 3: The Sorcerer's Curse
Get Hard
Code Geass: Akito the Exiled 3: The Brightness Falls
The Treacherous
Containment
Steve Jobs
Kidnapping Mr. Heineken
Bad Asses on the Bayou
Barely Lethal
Haikyuu!! The Movie: The End and the Beginning
Infini
Sisters
Do You Believe?
Vendetta
Careful What You Wish For
Monster High: Haunted
Frozen Fever
Huevos: Little Rooster's Egg-Cellent Adventure
Teen Beach 2
The Scorpion King 4: Quest for Power
Mississippi Grind
While We're Young
The Smell of Us
Hyena Road
Wild Horses
Coin Locker Girl
SPL 2: A Time for Consequences
Martyrs
A Man Called Ove
Insurgent
Love & Mercy
Furious 7
Little Boy
Ricki and the Flash
Extraction
3-Headed Shark Attack
The Final Girls
The Hateful Eight
Black Mass
The Big Short
Cobain: Montage of Heck
Joy
The Girl in the Book
Bridge of Spies
Point Break
10000 Years Later
Winter on Fire: Ukraine's Fight for Freedom
Fifty Shades of Grey
No Escape
The Atticus Institute
Jupiter Ascending
The Little Prince
Blackhat
Howl
Dead Rising: Watchtower
Criminal Activities
Summertime
Far from the Madding Crowd
Akron
Shaun the Sheep Movie
Alvin and the Chipmunks: The Road Chip
X-Rated: The Greatest Adult Movies of All Time
Chronicles of the Ghostly Tribe
Paper Towns
Bajirao Mastani
A Delicious Flight
Everest
Mr. Holmes
Tale of Tales
Palm Trees in the Snow
The Lady in the Van
Love the Coopers
Tangerine
The Longest Ride
Curve
Minions
How to Plan an Orgy in a Small Town
The Transporter Refueled
Open Season: Scared Silly
Kept Woman
Orange
The Empire of Corpses
The Wedding Ringer
Maze Runner: The Scorch Trials
The Revenant
Vice
Mune: Guardian of the Moon
Poltergeist
Extinction
The Benefactor
Seeds of Yesterday
Crown for Christmas
If There Be Thorns
The Neon Demon
The Light Between Oceans
Gods of Egypt
First Girl I Loved
Girl in the Box
Joshy
The Land Before Time XIV: Journey of the Brave
Café Society
The Red Thread
Keanu
White Lily
Bad Cat
How to Be Single
Kizumonogatari Part 1: Tekketsu
Being 17
Navy Seals vs. Zombies
The Angry Birds Movie
Gold
Dou kyu sei – Classmates
Call of Heroes
League of Gods
Billy Lynn's Long Halftime Walk
Wet Woman in the Wind
Justice League vs. Teen Titans
Christine
Deadly Pickup
Rogue One: A Star Wars Story
Kung Fu Panda 3
A Hologram for the King
L.O.R.D: Legend of Ravaging Dynasties
Love & Friendship
Eddie the Eagle
LEGO Jurassic World: The Indominus Escape
The Handmaiden
The Forest
Star Trek Beyond
Monolith
Sultan
Ratchet & Clank
My Life as a Zucchini
Risen
God's Not Dead 2
Monster Trucks
Viral
Blue Mountain State: The Rise of Thadland
Kindergarten Cop 2
Personal Shopper
A Monster Calls
The Boss
Under the Shadow
The Monkey King 2
Ice Age: Collision Course
Julieta
The Great Wall
Miss Sloane
Adulterers
Sniper: Special Ops
Sing Street
Rules Don't Apply
Green Room
Allegiant
Echo Torch
Ice Age: The Great Egg-Scapade
Rebirth
Captain Fantastic
The Other Side of the Door
Survival Family
Keeping Up with the Joneses
Independence Day: Resurgence
Sherlock: The Abominable Bride
Monster High: Great Scarrier Reef
Alone in Berlin
Teenage Mutant Ninja Turtles: Out of the Shadows
Trolls
Alice Through the Looking Glass
Brimstone
Science Fiction Volume One: The Osiris Child
The Choice
Maggie's Plan
When the Bough Breaks
Kill Command
Loving
Rock Dog
Dear Zindagi
Sniper: Ghost Shooter
Patient Seven
Collide
Miracles from Heaven
ARQ
Deepwater Horizon
Absolutely Fabulous: The Movie
The Mermaid
Live by Night
Last Days in the Desert
Frantz
USS Indianapolis: Men of Courage
Marauders
Patriots Day
Friend Request
Nerve
Allied
Fallen
Criminal
Railroad Tigers
The Ten Commandments: The Movie
Popstar: Never Stop Never Stopping
Jarhead 3: The Siege
Doctor Strange
Suicide Squad
The Birth of a Nation
The Duelist
Kung Fu Panda: Secrets of the Scroll
In Her Name
We Are the Flesh
No manches, Frida
The Assignment
American Honey
The Last King
Panfilov's 28 Men
Hacker
Batman v Superman: Dawn of Justice
The Young Messiah
Pandora
Mechanic: Resurrection
I Am a Hero
Inferno
Pet
Full Speed
The Visitors: Bastille Day
The Icebreaker
Pokémon the Movie: Volcanion and the Mechanical Marvel
Moana
Robinson Crusoe
Boyka: Undisputed IV
Almost Christmas
Hunt for the Wilderpeople
Scooby-Doo! and WWE: Curse of the Speed Demon
Underworld: Blood Wars
Grease Live
The Fundamentals of Caring
Moonlight
Collateral Beauty
The Magnificent Seven
Luck-Key
Fences
Genius
They Call Me Jeeg
Everybody Wants Some!!
Crouching Tiger, Hidden Dragon: Sword of Destiny
Hail, Caesar!
Operation Chromite
Goodbye Berlin
Me Before You
Super Family
After the Storm
Before the Flood
La La Land
The Invitation
Code Geass: Akito the Exiled 5: To Beloved Ones
Trespass Against Us
Maximum Ride
Elle
Pandemic
The Mountain II
Barbershop: The Next Cut
The Shallows
Up for Love
Raw
Terra Formars
From the Land of the Moon
Now You See Me 2
Sully
Mine
Flight Crew
Bad Moms
Money Monster
The Conjuring 2
The Witch
Tiger, Blood in the Mouth
Blood Father
The Founder
Pride and Prejudice and Zombies
Max Steel
Middle School: The Worst Years of My Life
Bleed for This
Anthropoid
Mother's Day
Zootopia
Hidden Kisses
The Gigolo 2
Assassination Classroom: Graduation
Demolition
P.S. Girls
Sing
Denial
Florence Foster Jenkins
Pelé: Birth of a Legend
The Huntsman: Winter's War
Never Back Down: No Surrender
I, Daniel Blake
One Piece Film: GOLD
LEGO DC Comics Super Heroes: Justice League - Gotham City Breakout
Dark Crimes
Your Name.
Queen of Katwe
Assassination Classroom the Movie: 365 Days' Time
Hidden Figures
Le Clitoris
Siren
I.T.
Fantastic Beasts and Where to Find Them
Monster High: Welcome to Monster High
My Little Pony: Equestria Girls - Legend of Everfree
Don't Breathe
War Dogs
Ben-Hur
Neighbors 2: Sorority Rising
Midnight Special
Norm of the North
Hush
Night of Something Strange
White Girl
Ghostbusters
Misconduct
High Strung
Barbie: Spy Squad
Captain America: Civil War
Operation Mekong
The Girl on the Train
Manchester by the Sea
Zoolander 2
Asura: The City of Madness
The BFG
Penny Pincher!
Doraemon: Nobita and the Birth of Japan
The Disappointments Room
Maudie
Skiptrace
The Masked Saint
The Man Who Knew Infinity
Home Invasion
Cave
Yu-Gi-Oh!: The Dark Side of Dimensions
Hacksaw Ridge
I'm Not Ashamed
10 Cloverfield Lane
Masterminds
Jack Reacher: Never Go Back
Down by Love
My Wife's Sister
Two Is a Family
Barry
I Am Wrath
X-Men: Apocalypse
Perfect Strangers
Paterson
Office Christmas Party
The Do-Over
Morgan
Peaky Blinders: The True Story
A Conspiracy of Faith
Big Fish & Begonia
The Age of Shadows
The Belko Experiment
Finding Dory
The Promise
The Limehouse Golem
Warcraft
Precious Cargo
Beauty Salon: Special Service
Pete's Dragon
Ouija: Origin of Evil
Kubo and the Two Strings
Mi prima la sexóloga
Batman: The Killing Joke
Ride Along 2
The Void
Crayon Shin-chan: Fast Asleep! Dreaming World Big Assault!
Assassin's Creed
Sheep & Wolves
At the End of the Tunnel
Fifty Shades of Black
Mai-chan's Daily Life
Nocturnal Animals
The Autopsy of Jane Doe
To Steal from a Thief
13 Hours: The Secret Soldiers of Benghazi
Russian Mom
Don't Kill It
Free State of Jones
The Accountant
Snowden
King Cobra
Sanam Teri Kasam
Jason Bourne
Five
It's Only the End of the World
Mr. Right
Sadako vs. Kayako
Why Him?
The Whole Truth
Dirty Grandpa
Cell
Adventures in Babysitting
The Love Witch
Bridget Jones's Baby
Tunnel
Lion
Adidas Vs. Puma: The Brother's Feud
My Friend's Mom
In This Corner of the World
The Swap
Train to Busan
The Edge of Seventeen
The 5th Wave
Jackie
Don't Blame the Kid
Swiss Army Man
Batman: Bad Blood
Kingsglaive: Final Fantasy XV
London Has Fallen
Nine Lives
RE:BORN
Grimsby
Cabin Fever
Rocco
Hands of Stone
The Legend of Tarzan
LEGO DC Comics Super Heroes: Justice League - Cosmic Clash
Kickboxer: Vengeance
Mr. Church
31
The Last Princess
Detective Conan: The Darkest Nightmare
The Finest Hours
Deadpool
Incarnate
Lady Macbeth
Taekwondo
Ballerina
Triple 9
Pavada
The Red Turtle
Sex Doll
Passengers
Shin Godzilla
A Street Cat Named Bob
For the Love of Spock
Blair Witch
The Nice Guys
A Silent Voice: The Movie
Spectral
Viking
I've Always Liked You
Don't Think Twice
Don't Hang Up
The Warriors Gate
Sword Master
Hell or High Water
In a Valley of Violence
Silence
The Beatles: Eight Days a Week - The Touring Years
Central Intelligence
The Purge: Election Year
Miss Peregrine's Home for Peculiar Children
The Infiltrator
She and Her Cat: Everything Flows
Before I Wake
Dangal
The Girl with All the Gifts
Like Crazy
The Day Naruto Became Hokage
A Cinderella Story: If the Shoe Fits
Storks
Arrival
The Lion Guard: Return of the Roar
Exposed
Bastille Day
GANTZ:O
Drowning Love
Piper
Sausage Party
Race
The Innocents
Mike and Dave Need Wedding Dates
Lights Out
Amateur Night
Hard Target 2
The Wailing
The Boy
The Siege of Jadotville
Unleashing Mr. Darcy
Within
My Big Fat Greek Wedding 2
Mom's Friend 2
Throne of Elves
The Secret Life of Pets
Death Note: Light Up the NEW World
The Red Pill
One Piece: Heart of Gold
The Jungle Bunch
Journey to the West: The Demons Strike Back
Geostorm
Roman J. Israel, Esq.
Savage Dog
Ayla: The Daughter of War
Sand Castle
V.I.P.
Brain on Fire
Battle of the Sexes
The Fortress
Okja
The Crucifixion
Antiporno
The Irregular at Magic High School: The Girl Who Summons the Stars
The Exception
Leatherface
The Merciless
The Little Vampire 3D
A Bad Moms Christmas
Gintama
Girls Trip
The Star
Tulip Fever
The Death of Stalin
A Bag of Marbles
The Hitman's Bodyguard
Bullet Head
Pirates of the Caribbean: Dead Men Tell No Tales
Deadpool: No Good Deed
The Dark Tower
Papillon
Stratton
Overdrive
Below Her Mouth
Napping Princess
Sex Plate 17
The Climb
Transformers: The Last Knight
The Mountain Between Us
From Straight A's to XXX
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Suburbicon
The Wall
Godzilla: Planet of the Monsters
Teen Titans: The Judas Contract
Loving Vincent
Surf's Up 2: WaveMania
Shockwave: Countdown to Disaster
Kuroko's Basketball the Movie: Last Game
Satan's Slaves
Security
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Message from the King
My Little Pony: The Movie
Palm Swings
One Cut of the Dead
The Space Between Us
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Guardians
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A Witches' Ball
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It
47 Meters Down
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Olaf's Frozen Adventure
Mektoub, My Love: Canto Uno
War Machine
Cargo
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Maze
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I, Tonya
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R.A.I.D. Special Unit
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Fireworks
Cinderella's Hot Night
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The 12th Man
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1987: When the Day Comes
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Film Stars Don't Die in Liverpool
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1922
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Escape Room
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Get Out
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The Circle
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Tour de Pharmacy
Woody Woodpecker
Picture of Beauty
Kill 'em All
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Psych: The Movie
All the Money in the World
Transformers: Titans Return
xXx: Return of Xander Cage
The Lodgers
Mary Shelley
The Lego Batman Movie
Star Wars: The Last Jedi
Pornocracy: The New Sex Multinationals
Power Rangers
Diary of a Wimpy Kid: The Long Haul
King Arthur: Legend of the Sword
The Babysitter
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Slumber
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Lady Bird
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The Square
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Rebel in the Rye
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Good Time
6 Days
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Alone
Puss in Book: Trapped in an Epic Tale
Naked
Ghost in the Shell
Call Me by Your Name
Veronica
The Battleship Island
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Tangled: Before Ever After
Batman and Harley Quinn
Logan
Fairy Tail: Dragon Cry
Death Race 2050
Attraction
Megan Leavey
Don't Knock Twice
Burning Sands
Killing Gunther
Coco
The Sisters S-Scandal
Real
First They Killed My Father
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Jungle
Valerian and the City of a Thousand Planets
Unlocked
Jumanji: Welcome to the Jungle
Jigsaw
Bright
Stronger
All Eyez on Me
The Beguiled
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Kingsman: The Golden Circle
Molly's Game
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Only the Brave
High Heel Homicide
Black Butler: Book of the Atlantic
Desire
Better Watch Out
Tehran Taboo
Captain Underpants: The First Epic Movie
Max 2: White House Hero
Destruction: Los Angeles
Adult Only Institute
Godard Mon Amour
LEGO Frozen Northern Lights
Song to Song
Mayhem
Cold Skin
John Wick: Chapter 2
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The Case for Christ
The Endless
Beach Rats
Fate/stay night: Heaven's Feel I. Presage Flower
God's Own Country
Once Upon a Time in Venice
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The Big Sick
Guardians of the Galaxy Vol. 2
After Porn Ends 2
Killing Ground
Phantom Thread
Memoir of a Murderer
The Only Living Boy in New York
Mark Felt: The Man Who Brought Down the White House
How to Be a Latin Lover
Everything, Everything
Mudbound
6 Below: Miracle on the Mountain
Bring It On: Worldwide #Cheersmack
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God: Serengeti
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2:22
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No Game No Life: Zero
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The Girl in the Fog
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S.W.A.T.: Under Siege
The Ash Lad: In the Hall of the Mountain King
Wish Upon
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Kong: Skull Island
Split
The Snowman
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An Ordinary Man
Super Dark Times
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Blade Runner: Black Out 2022
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Hangman
Happy Death Day
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#realityhigh
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While the Wolf's Away
The Son of Bigfoot
Baby Driver
Let Me Eat Your Pancreas
T2 Trainspotting
Smurfs: The Lost Village
Life
JoJo's Bizarre Adventure: Diamond is Unbreakable – Chapter 1
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Brotherhood of Blades II: The Infernal Battlefield
The Shack
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Loveless
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Paddington 2
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The Incredible Story of the Giant Pear
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A Cure for Wellness
mother!
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Outlaw King
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An Interview with God
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Level 16
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Teen Titans Go! To the Movies
My Hero Academia: Two Heroes
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Tomb Raider
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Forever My Girl
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The Professor
Crazy Rich Asians
Tully
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Avengers: Infinity War
Ben Is Back
Second Act
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The Strangers: Prey at Night
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Every Day
Suicide Squad: Hell to Pay
Utøya: July 22
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Scooby-Doo! & Batman: The Brave and the Bold
Looking Glass
Benji
Paris Pigalle
How It Ends
Colette
The Spy Who Dumped Me
Black Panther
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God, Sex and Truth
Peppermint
Everybody Knows
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Mary Queen of Scots
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Game Night
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7 Guardians of the Tomb
At Eternity's Gate
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Mirai
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A Quiet Place
Ready Player One
211
A Star Is Born
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Terminal
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The Emperor of Paris
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Little Italy
Bohemian Rhapsody
Holmes & Watson
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I Am Not an Easy Man
Alpha
If Beale Street Could Talk
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Fifty Shades Freed
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When We First Met
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Summer of 84
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Mandy
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Midnight Sun
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Unbroken: Path to Redemption
Bad Times at the El Royale
Anon
I Want to Eat Your Pancreas
All the Devil's Men
Terrifier
A Prayer Before Dawn
Vice
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Bungo Stray Dogs: Dead Apple
Be with You
Beautiful Vampire
Maze Runner: The Death Cure
Carmen & Lola
High Society
Cold Feet
The Domestics
Spinning Man
Code Geass: Lelouch of the Rebellion – Transgression
Padmaavat
Checkered Ninja
White Boy Rick
55 Steps
Euphoria
Death Race: Beyond Anarchy
Green Book
Backtrace
Life of the Party
Eighth Grade
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Gonjiam: Haunted Asylum
The Cloverfield Paradox
Hereditary
Ant-Man and the Wasp
The House That Jack Built
Early Man
The Clovehitch Killer
Batman Ninja
Ocean's Eight
Hostile
Andhadhun
The Monkey King 3
Lust Stories
Clara
Galveston
Mowgli: Legend of the Jungle
Sicario: Day of the Soldado
Billionaire Boys Club
A-X-L
Apostle
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Downrange
Other Side of the Box
Doraemon: Nobita's Treasure Island
Re:Zero - Starting Life in Another World Memory Snow
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The Night Eats the World
Detective Dee: The Four Heavenly Kings
Peter Rabbit
Black Water
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Instant Family
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Replicas
American Animals
Roma
Sorry to Bother You
We
Deep Blue Sea 2
Game Over, Man!
The Christmas Chronicles
The Current War
Border
Kursk
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I Feel Pretty
Robin Hood
Den of Thieves
Can You Ever Forgive Me?
A Simple Favor
Book Club
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Tumbbad
Okko's Inn
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UFO
God's Not Dead: A Light in Darkness
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Fantastic Beasts: The Crimes of Grindelwald
Taxi 5
Accident Man
Constantine: City of Demons - The Movie
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Overboard
Mary Magdalene
Mirage
Possum
I Can Only Imagine
Ordinary People
The Guernsey Literary & Potato Peel Pie Society
The 15:17 to Paris
Assassination Nation
Unfriended: Dark Web
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The Magic Tuche
Monster Hunt 2
Kin
Mary Poppins Returns
The Bad Seed
Halloween
The Nutcracker and the Four Realms
Johnny English Strikes Again
Isle of Dogs
Hotel Transylvania 3: Summer Vacation
Backstabbing for Beginners
May the Devil Take You
Adrift
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The Conductor
My Teacher, My Obsession
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Ride
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The Testament of Sister New Devil: Departures
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The Hate U Give
Black Mirror: Bandersnatch
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Swiped
22 July
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London Fields
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Ralph Breaks the Internet
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Braven
Wildlife
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Deadpool 2
Traffik
Death Wish
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A Futile and Stupid Gesture
Won't You Be My Neighbor?
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Christopher Robin
Never Look Away
Patient Zero
Tau
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Tag
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Hell Fest
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The Vanishing of Sidney Hall
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Paul, Apostle of Christ
Disobedience
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The Tree of Blood
Adultery Alumni Association
Just Friends
The Witch: Part 1. The Subversion
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Inuyashiki
Venom
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Incredibles 2
Once Upon a Deadpool
Illang: The Wolf Brigade
Samson
The Miseducation of Cameron Post
Upgrade
The Beach House
Psychokinesis
Along with the Gods: The Last 49 Days
The House with a Clock in Its Walls
K.G.F: Chapter 1
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Geetha Govindam
Unstoppable
Terrified
Batman: Gotham by Gaslight
Mile 22
Overlord
The Meg
A Wrinkle in Time
Be My Master
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Genghis Khan
7 Days in Entebbe
The Nightingale
An Affair
Air Strike
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The Drug King
Winchester
Believer
The Last Sharknado: It's About Time
Time Trap
Extinction
Natsume's Book of Friends: Ephemeral Bond
Truth or Dare
Spider-Man: Into the Spider-Verse
Blockers
A Private War
To All the Boys I've Loved Before
The Night Comes for Us
The Captain
12 Feet Deep
Red Sparrow
Revenger
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Tremors: A Cold Day in Hell
Code Geass: Lelouch of the Rebellion – Glorification
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Bad Samaritan
Acrimony
After Porn Ends 3
The Hurricane Heist
The Girl in the Spider's Web
Under the Sea: A Descendants Story
The Ballad of Buster Scruggs
Widows
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The Farewell
Adultery Alumni Association 2
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Ophelia
The Dead Don't Die
Spider-Man: Far From Home
Happy Death Day 2U
Terminator: Dark Fate
A Dog's Way Home
Greed
The Courier
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Kingdom
Trial by Fire
6 Underground
Scooby-Doo! Return to Zombie Island
The Kitchen
In the Tall Grass
Code Geass: Lelouch of the Re;Surrection
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4x4
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The Curse of La Llorona
The Kill Team
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Hello World
Hacker
The Perfect Date
Wonder Woman: Bloodlines
Godzilla: King of the Monsters
Last Breath
Judy
Oh, Ramona!
Teen Titans Go! vs. Teen Titans
Charlie's Angels
21 Bridges
Burn
Yummy
My Hero Academia: All Might Rising
The Divine Fury
Maleficent: Mistress of Evil
Cats
Vivarium
Queen of Hearts
Playing with Fire
Cold Blood
Life Like
My Grandpa is an Alien
The Lighthouse
Polaroid
Bombshell
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The Golden Glove
The Sun Is Also a Star
The Vanishing
The Blackout
Porno
Overcomer
Jexi
Once Upon a Time... in Hollywood
Lev Yashin. The Dream Goalkeeper
Us
The Game Changers
Queen & Slim
The Last Summer
Escape Plan: The Extractors
My Hero Academia: Heroes Rising
The Aeronauts
Iron Mask
Violet Evergarden: Eternity and the Auto Memory Doll
Rattlesnake
Corpus Christi
Skin
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Heroic Losers
The Best of Enemies
Zombieland: Double Tap
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Freaks
Penoza: The Final Chapter
Dark Phoenix
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A Dog's Journey
Rust Creek
Good Boys
Re:Zero - Starting Life in Another World The Frozen Bond
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Abigail
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Fractured
The Man Who Killed Hitler and Then the Bigfoot
Serenity
Just Mercy
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Vita & Virginia
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Pokémon Detective Pikachu
The Dude in Me
Is It Wrong to Try to Pick Up Girls in a Dungeon?: Arrow of the Orion
Kitbull
Street Flow
Finding Steve McQueen
Alita: Battle Angel
Doctor Sleep
It Chapter Two
Ford v Ferrari
Dora and the Lost City of Gold
Pain and Glory
Fast & Furious Presents: Hobbs & Shaw
The Professor and the Madman
Eli
Foxter and Max
I Spit on Your Grave: Déjà Vu
Shaft
Jay and Silent Bob Reboot
I See You
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War
Hustlers
Black and Blue
Unplanned
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Rabid
A Cinderella Story: Christmas Wish
Buñuel in the Labyrinth of the Turtles
Little Monsters
Playmobil: The Movie
Love at Second Sight
Our Friend
Glass
Someone Great
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Ip Man 4: The Finale
47 Meters Down: Uncaged
Little
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Spies in Disguise
The Addams Family
Harriet
Tune in for Love
Inside Man: Most Wanted
Haunt
Deadwood: The Movie
Svaha: The Sixth Finger
The Art of Self-Defense
Apollo 11
Serial (Bad) Weddings 2
Hellboy
Uncut Gems
Missing Link
The Lego Movie 2: The Second Part
Loop
The Intruder
Blinded by the Light
No manches, Frida 2: paraíso destruido
Dumbo
QT8: The First Eight
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The Gangster, the Cop, the Devil
Don't Let Go
In the Arms of an Assassin
Line of Duty
Polar
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The Captain
Jarhead: Law of Return
Jojo Rabbit
True History of the Kelly Gang
Scooby-Doo! and the Curse of the 13th Ghost
Black Christmas
What Men Want
John Wick: Chapter 3 - Parabellum
KONOSUBA – God's blessing on this wonderful world! Legend of Crimson
Blood Quantum
Dogs Don't Wear Pants
The Irishman
Men in Black: International
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Child's Play
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Where'd You Go, Bernadette
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The Angry Birds Movie 2
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Ma
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Little Joe
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Night Hunter
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A Shaun the Sheep Movie: Farmageddon
Batman: Hush
Pinocchio
Miracle in Cell No. 7
Danger Close: The Battle of Long Tan
Above Suspicion
UglyDolls
The Souvenir
Code 8
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A Score to Settle
Isn't It Romantic
The Highwaymen
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The Lion King
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Monos
Noelle
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How to Train Your Dragon: Homecoming
Always Be My Maybe
Hope Gap
Rascal Does Not Dream of a Dreaming Girl
Sadako
1942: Unknown Battle
Primal: Tales of Savagery
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Toy Story 4
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Official Secrets
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Kim Ji-young, Born 1982
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Down
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Richard Jewell
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Can You Keep a Secret?
Tall Girl
Ne Zha
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Velvet Buzzsaw
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Better Days
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La Belle Époque
Captain Marvel
1917
Buddy Games
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Jumanji: The Next Level
Her Blue Sky
Badland
AquaSlash
The Secret Life of Pets 2
Escaping the Madhouse: The Nellie Bly Story
Falling Inn Love
Scary Stories to Tell in the Dark
Miss Bala
Demon Slayer: Kimetsu no Yaiba – Sibling's Bond
Blue Story
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Last Christmas
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I Lost My Body
Close
Angel Has Fallen
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Descendants 3
Yesterday
I Believe
The Bad Guys: Reign of Chaos
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Break
Midsommar
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Lying and Stealing
High & Low The Worst
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Dolemite Is My Name
Little Women
I Am Mother
Parasite
Father There Is Only One
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Z
Midway
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Sweetheart
Steven Universe: The Movie
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A Beautiful Day in the Neighborhood
It Must Be Heaven
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Hell House LLC III: Lake of Fire
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The Aftermath
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Uri: The Surgical Strike
Robert the Bruce
Plus One
Fall in Love at First Kiss
An Affair to Die For
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Annabelle Comes Home
Above the Shadows
Let It Snow
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Aladdin
Game of Thrones: The Last Watch
Color Out of Space
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Balkan Line
After
Point Blank
I'm No Longer Here
How to Train Your Dragon: The Hidden World
Hotel Mumbai
In the Shadow of the Moon
Ip Man: Kung Fu Master
Impetigore
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Anna
Escape Room
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7500
Iron Sky: The Coming Race
Batman vs Teenage Mutant Ninja Turtles
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IO
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Brian Banks
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The Summers of It – Chapter Two: It Ends
It Feels So Good
The Dirt
Girls with Balls
Cold Pursuit
We Die Young
Kabaneri of the Iron Fortress: The Battle of Unato
House of Hummingbird
The Knight Before Christmas
Persian Lessons
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Pawn
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Josee, the Tiger and the Fish
My Sex Doll
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Josée
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Raat Akeli Hai
I'm Thinking of Ending Things
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You Should Have Left
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Go!
Let Them All Talk
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Tenet
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Deliver Us from Evil
Beyond the Infinite Two Minutes
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Scoob!
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Never Rarely Sometimes Always
Breach
My Spy
Batman: Death in the Family
Jiang Ziya
Fukushima 50
The Last Frontier
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Phineas and Ferb the Movie: Candace Against the Universe
The Eight Hundred
Goblin Slayer: Goblin's Crown
All About Sex
Innocence
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Antebellum
Deathstroke: Knights & Dragons - The Movie
Mulan
The Tax Collector
Happy Halloween, Scooby-Doo!
Shortcut
Fatman
Wild Mountain Thyme
Survive the Night
American Pie Presents: Girls' Rules
Fatima
Debt Collectors
R-Rated Idol Seung-ha's Sex Scandal
David Attenborough: A Life on Our Planet
The Trial of the Chicago 7
The Croods: A New Age
Kill Chain
Two Distant Strangers
Sonic the Hedgehog
The Gentlemen
Occupation: Rainfall
Rebecca
Close Encounters of the Fifth Kind
Doraemon: Nobita's New Dinosaur
Ravage
The Silencing
Host
Don't Listen
The Lost Prince
The Heist of the Century
Howling Village
Patricia, A Hidden Passion
A Babysitter's Guide to Monster Hunting
Sound of Metal
Monsters of Man
Infamous
A Secret Love
Female Urologists 2
Skylines
The High Note
The Secrets We Keep
Spree
Misbehaviour
Wolfwalkers
High Ground
2067
Inheritance
Run Sweetheart Run
Beasts Clawing at Straws
Love Wedding Repeat
Once Upon a Snowman
Enola Holmes
Secret Society of Second Born Royals
On the Rocks
Riders of Justice
The Empty Man
Artemis Fowl
What Lies Below
Z-O-M-B-I-E-S 2
Medusa
Underwater
Mortal Kombat Legends: Scorpion's Revenge
Vanguard
PG: Psycho Goreman
The Midnight Sky
Bad Boys for Life
Sky High
The Banker
LEGO DC: Shazam! Magic and Monsters
Becky
The Swordsman
Alone
Coma
A California Christmas
Proximity
The Social Dilemma
Stand by Me Doraemon 2
Rose Island
Girl
An American Pickle
Barbie: Princess Adventure
Onward
Sky Sharks
Irresistible
The Broken Hearts Gallery
Patients of a Saint
Brothers by Blood
Let Him Go
Honest Thief
We Bare Bears: The Movie
Bigfoot Family
Shock Wave 2
All the Bright Places
Young Mother 5
Happiest Season
American Murder: The Family Next Door
Violet Evergarden: The Movie
BLACKPINK: Light Up the Sky
The Yin-Yang Master: Dream of Eternity
Superman: Red Son
Miss Americana
Tremors: Shrieker Island
Demon Slayer -Kimetsu no Yaiba- The Movie: Mugen Train
I Still Believe
Asphalt Burning
Trolls World Tour
The Invisible Man
First Cow
Night in Paradise
Feel the Beat
Sergio
The Turning
Adultery Alumni Association 3
Saint Maud
Dangerous Lies
The Half of It
Out of My League
The Sleepover
Upside-Down Magic
The Main Event
Lamp Life
Solitary
Death of Me
One Night in Miami...
Crazy, Not Insane
Gabriel's Inferno
Fatale
The Christmas Chronicles: Part Two
Digimon Adventure: Last Evolution Kizuna
The Call
The 2nd
Dolittle
The Wrong Missy
Borat Subsequent Moviefilm
The Father
Legacy of Lies
Return of the Tooth Fairy
Breaking Surface
Aunt's Temptation 3
Brahms: The Boy II
Black Water: Abyss
Ma Rainey's Black Bottom
Deep Blue Sea 3
Fate/stay night: Heaven's Feel III. Spring Song
The Other Lamb
Night Shift
Pieces of a Woman
Charm City Kings
Time to Hunt
Rogue
The Dark and the Wicked
The Owners
The Prom
My First Summer
Made in Abyss: Dawn of the Deep Soul
Invasion
Pixie
Project Power
The Occupant
Superman: Man of Tomorrow
Spenser Confidential
The Stranger by the Shore
Horizon Line
The Last Full Measure
True Mothers
Xico's Journey
Hamilton
A Fall from Grace
Swallow
One Night in Bangkok
Operation Christmas Drop
Given
Sniper: Assassin's End
Rich in Love
Hillbilly Elegy
Jingle Jangle: A Christmas Journey
Tesla
Unhinged
Lucy Shimmers and the Prince of Peace
Chemical Hearts
The Paramedic
Bill & Ted Face the Music
Sister-in-law's Taste
Sputnik
Greenland
Gretel & Hansel
Justice League Dark: Apokolips War
Books of Blood
Rising High
Children of the Corn
Disclosure
Cosmoball
Possessor
Over the Sky
The Water Man
The Hunt
The Secret Garden
Capone
Promising Young Woman
MOTHER
The Outpost
Work It
Making The Witcher
Made in Italy
Body Cam
The Old Guard
We Can Be Heroes
Synchronic
Mortal
After We Collided
Palm Springs
The Kissing Booth 2
Extraction
News of the World
A Whisker Away
Arthur & Merlin: Knights of Camelot
Hunter Hunter
Dinner in America
Magic Camp
Curious George: Go West, Go Wild
Freaks – You're One of Us
The War with Grandpa
Pokémon the Movie: Secrets of the Jungle
Sightless
Mare
Altered Carbon: Resleeved
If Anything Happens I Love You
Like a Boss
Love and Monsters
The Decline
Kalashnikov AK-47
Run
The Forgotten Battle
Joe Bell
Lost Girls & Love Hotels
Baba Yaga: Terror of the Dark Forest
The New Mutants
The Lodge
Black Beauty
Yes, God, Yes
All My Life
Isi & Ossi
The Rhythm Section
Think Like a Dog
The Night Clerk
Two by Two: Overboard!
365 Days
Spontaneous
To All the Boys: P.S. I Still Love You
Miraculous World: New York, United HeroeZ
Fantasy Island
Ammonite
Money Heist: The Phenomenon
100% Wolf
Father There Is Only One 2
Soul
The Larva Island Movie
Red
The Last Thing He Wanted
ariana grande: excuse me, i love you
Half Brothers
How to Be a Good Wife
Black Box
Lost Bullet
PAW Patrol: Jet to the Rescue
Black Is King
The Devil All the Time
Spell
Peninsula
Sea Fever
Thou Shalt Not Hate
R-Rated Idol Seung-ha's Fancy Walk
Relic
Da 5 Bloods
The Craft: Legacy
Birds of Prey (and the Fantabulous Emancipation of One Harley Quinn)
Let It Snow
Your Name Engraved Herein
2 Hearts
The One and Only Ivan
A Nice Girl Like You
Follow Me
Nowhere Special
Eurovision Song Contest: The Story of Fire Saga
Offering to the Storm
32 Malasana Street
Another Round
A Werewolf in England
The Last Days of American Crime
#Alive
The Kid Detective
Summer of 85
Life in a Year
Hitman: Agent Jun
Freaky
Bosomy Mom
Guns Akimbo
18 Year Old Seungha's Easy Piece of Cake
The King of Staten Island
Emma.
The Last Warrior: Root of Evil
Godmothered
Monster Hunter
Cadaver
The Assistant
The SpongeBob Movie: Sponge on the Run
Blithe Spirit
Mank
The Call of the Wild
Force of Nature
Holidate
The Rental
How I Became a Superhero
Last Moment of Clarity
Psycho-Pass 3: First Inspector
The Babysitter: Killer Queen
The Courier
Dragonheart: Vengeance
The Promised Neverland
The Princess Switch: Switched Again
Wonder Woman 1984
Escape from Pretoria
Songbird
Stepmom's Desire
The Vanished
Coven
Hubie Halloween
The Return
Dangerous Drugs of Sex
Ben 10 vs. the Universe: The Movie
Amulet
Superdeep
Roald Dahl's The Witches
Over the Moon
Shadow in the Cloud
Clouds
Resistance
Alone
The Acrobat
Words on Bathroom Walls
Summerland
Flashback
Nobody Sleeps in the Woods Tonight
Calm with Horses
Gabriel's Inferno: Part III
His House
Drive My Car
Billie Eilish: The World's a Little Blurry
Confessions of an Invisible Girl
Coming Home in the Dark
Jolt
Silent Night
Help! My In-Laws Are Vampires!
Fear Street: 1978
Major Grom: Plague Doctor
Don't Look Up
The French Dispatch
Seaspiracy
Prey
A Journal for Jordan
Sword Art Online the Movie – Progressive – Aria of a Starless Night
Nightbooks
The White Tiger
Hunters
The Vault
Nomadland
Sweet Girl
Dangerous
The Innocents
Miraculous World: Shanghai – The Legend of Ladydragon
Koati
The Simpsons in Plusaversary
Nobody
Rogue Hostage
Kingdom: Ashin of the North
Eternals
Back to the Outback
The Misfits
Miracle: Letters to the President
Chaos Walking
Antlers
Old
Detective Chinatown 3
One Shot
Agnes
Counterattack
Blood Red Sky
Straight Outta Nowhere: Scooby-Doo! Meets Courage the Cowardly Dog
Nobody Sleeps in the Woods Tonight 2
Those Who Wish Me Dead
Lansky
Fortress
Hard Hit
Friends: The Reunion
A Quiet Place Part II
Spoiled Brats
The Worst Person in the World
To All the Boys: Always and Forever
Barb & Star Go to Vista Del Mar
Jungle Cruise
The Conjuring: The Devil Made Me Do It
The Night House
The Cursed
Hypnotic
Apex
Cosmic Sin
Tom Clancy's Without Remorse
The Seven Deadly Sins: Cursed by Light
A Boy Called Christmas
Monster Pets: A Hotel Transylvania Short
Cherry
Zack Snyder's Justice League
Worth
Wrong Turn
Sing 2
State of Siege: Temple Attack
The Deep House
Drishyam 2
Diabolik
Malignant
Ciao Alberto
Titane
TINA
Sweet & Sour
Las Leyendas: El Origen
Palmer
South Park: Post COVID: The Return of COVID
Coming 2 America
Ghostbusters: Afterlife
Trollhunters: Rise of the Titans
Detective Conan: The Scarlet Bullet
Around the World in 80 Days
Ride or Die
Deception
Oxygen
Queenpins
The Desperate Hour
Vivo
Dynasty Warriors
The Girl Who Believes in Miracles
Deadly Illusions
Pelé
Granit
The Addams Family 2
Till Death
Licorice Pizza
He's All That
Batman: The Long Halloween, Part Two
Respect
Idol Seungha's R-Rated Film Shooting
Vacation Friends
Risen
The Serpent
Stowaway
Pil's Adventures
Reminiscence
Trigger Point
Little Fish
The Many Saints of Newark
Mortal Kombat Legends: Battle of the Realms
The Advent Calendar
Sooryavanshi
A Writer's Odyssey
Munich – The Edge of War
Red Rocket
F9
Mousa
Teen Titans Go! See Space Jam
Lamb
Ip Man: The Awakening
Batman: Soul of the Dragon
Recalled
Ghost Lab
The Medium
Encanto
Cry Macho
Outside the Wire
Planet Dune
The Guilty
Judas and the Black Messiah
Megaboa
V/H/S/94
Girl in the Basement
Barbie: Big City, Big Dreams
Vanquish
My Lovely Angel
Luca
Below Zero
The Unforgivable
13 Minutes
SAS: Red Notice
My Little Pony: A New Generation
The Princess Switch 3: Romancing the Star
Clifford the Big Red Dog
Simple Passion
Godzilla vs. Kong
Christmas with the Tuches
Moxie
The Girl on the Train
Apache Junction
Dark Web: Cicada 3301
Space Sweepers
Extinct
King Richard
Tom & Jerry
The Good, the Bart, and the Loki
Man in Love
My Sweet Monster
No Man of God
Candyman
Willy's Wonderland
Time Is Up
Raging Fire
PAW Patrol: The Movie
The Map of Tiny Perfect Things
Black Box
Eva
The Alpinist
Finding You
Ferry
Castle Falls
Raya and the Last Dragon
Tokyo Revengers
Army of Thieves
Great White
House of Gucci
The Dry
Us Again
Awake
Escape Room: Tournament of Champions
Paranormal Activity: Next of Kin
There's Someone Inside Your House
Free Guy
Jujutsu Kaisen 0
Firebird
Descendants: The Royal Wedding
Nightmare Alley
The Ex
Brain Freeze
To Kill the Beast
Family Swap
Seal Team
Upon the Magic Roads
Heart of Champions
The Forever Purge
Restart the Earth
Ghost Mansion
Mainstream
The Power of the Dog
CODA
Dogtanian and the Three Muskehounds
The Colony
The Legend of the Christmas Witch: The Origins
Werewolves Within
The Electrical Life of Louis Wain
The Warrior Princess
The Voyeurs
New Gods: Nezha Reborn
Secret Magic Control Agency
AINBO: Spirit of the Amazon
Diary of a Wimpy Kid
Toma
The World to Come
Evangelion: 3.0+1.0 Thrice Upon a Time
Space Jam: A New Legacy
Dakaichi: I'm Being Harassed by the Sexiest Man of the Year—The Movie: In Spain
Injustice
Venom: Let There Be Carnage
Prisoners of the Ghostland
Caveat
Mobile Suit Gundam Hathaway
Trolls Holiday in Harmony
The Unholy
Monster's Battlefield
The Immortal Wars: Rebirth
Catman
Boss Level
A Dog Named Palma
The Stronghold
The Virtuoso
Black Widow
Hitman's Wife's Bodyguard
The Last Duel
The Ice Road
Summer Ghost
Pretty Guardian Sailor Moon Eternal the Movie Part 1
The Suicide Squad
The Hating Game
Fear Street: 1666
The Monkey King: Reborn
No One Gets Out Alive
Minari
The Last Letter from Your Lover
Rumble
Nothing Serious
More the Merrier
Fatherhood
Fib the Truth
Crisis
Meteor
Mortal Kombat
Return of Special Forces 5
Naked Singularity
South of Heaven
Last Night in Soho
Spider-Man: No Way Home
22 vs. Earth
Red Notice
Never Back Down: Revolt
Dark Spell
The Woman in the Window
The Kissing Booth 3
Escape from Mogadishu
Snake Eyes: G.I. Joe Origins
The Mad Women's Ball
Heart Beats
Pushpa: The Rise - Part 1
Bull
Death Alley
Benedetta
Redemption Day
After We Fell
The Wolf and the Lion
Dune
Spencer
The Resort
Hell Hath No Fury
Kate
The Green Knight
The Banishing
West Side Story
The Whole Truth
Happening
An Egg Rescue
Shang-Chi and the Legend of the Ten Rings
Rurouni Kenshin: The Beginning
The Witcher: Nightmare of the Wolf
Land
The Mauritanian
Shaun the Sheep: The Flight Before Christmas
We Made a Beautiful Bouquet
Endangered Species
Separation
Don't Kill Me
The Little Things
The Dig
Rurouni Kenshin: The Final
Being James Bond
Night Teeth
Midnight
My Hero Academia: World Heroes' Mission
Finch
Xtreme
Monster Family 2
Voyagers
Ron's Gone Wrong
The United States vs. Billie Holiday
Chernobyl: Abyss
A Week Away
The Marksman
Home Sweet Home Alone
12 Mighty Orphans
JJ+E
Dear Evan Hansen
No Time to Die
The Last Mercenary
Gunpowder Milkshake
The Duke
My Son
The Protégé
The Accursed
Still Out of My League
The Fallout
24 Year Old Yoon Yul's Sexy Breasts
Run Hide Fight
Natsume's Book of Friends: The Waking Rock and the Strange Visitor
tick, tick... BOOM!
Deep
The Sadness
Belfast
The 8th Night
Resident Evil: Welcome to Raccoon City
Resort to Love
Furioza
Pleasure
Wish Dragon
The Boss Baby: Family Business
The Loud House Movie
Copshop
Cloudy Mountain
Cube
Peter Rabbit 2: The Runaway
Wrath of Man
Bliss
Serial (Bad) Weddings 3
The Tragedy of Macbeth
Last Man Down
Doors
Earwig and the Witch
Mission: Possible
The Tomorrow War
Belle
Boiling Point
Don't Breathe 2
Small World
Cruella
Green Snake
Zone 414
Infinite
Cinderella
Army of the Dead
No Sudden Move
American Underdog
Seobok
Sinkhole
The Battle at Lake Changjin
Malcolm & Marie
The Burning Sea
Swan Song
Halloween Kills
The Djinn
The Harder They Fall
Gone Mom: The Disappearance of Jennifer Dulos
YES DAY
Settlers
Spirit Untamed
Words Bubble Up Like Soda Pop
Spiral: From the Book of Saw
Old Henry
Batman: The Long Halloween, Part One
The Mitchells vs. the Machines
The King's Man
Midnight in the Switchgrass
Parallel Mothers
Zola
The Shadow in My Eye
Finding ʻOhana
Hilda and the Mountain King
The Matrix Resurrections
Wicked
I Care a Lot
Fear Street: 1994
Justice Society: World War II
Asakusa Kid
Stillwater
Quo Vadis, Aida?
The Good Nurse
The School for Good and Evil
The Ledge
The Requin
Marcel the Shell with Shoes On
Secret Headquarters
Look Both Ways
The Man from Toronto
Monster High: The Movie
Babylon
X
Heatwave
The Magic Flute
Mira
Through My Window
R.I.P.D. 2: Rise of the Damned
Pearl
Ennio
Memory
Drifting Home
The Key Game
Hocus Pocus 2
House of Darkness
The Pale Blue Eye
The Boy, the Mole, the Fox and the Horse
Encanto at the Hollywood Bowl
Black Adam
No Limit
Confess, Fletch
The Commando
Loving Adults
Jeepers Creepers: Reborn
Shattered
Special Delivery
4 Horsemen: Apocalypse
Moonfall
K.G.F: Chapter 2
Hustle
M3GAN
Seoul Vibe
Scrooge: A Christmas Carol
Wire Room
Little Eggs: A Frozen Rescue
Bullet Train
The Killer
Alienoid
Cyber Hell: Exposing an Internet Horror
The Lair
Lost Bullet 2
Fantastic Beasts: The Secrets of Dumbledore
Tad, the Lost Explorer and the Emerald Tablet
Speak No Evil
Lonely Castle in the Mirror
About Fate
DC League of Super-Pets
Uncharted
Kantara
Don't Look at the Demon
Trick or Treat Scooby-Doo!
Mutant: Ghost War Girl
WarHunt
Spirited
Eradication
Savage Salvation
The Black Phone
Bandit
The One
Texas Chainsaw Massacre
Fistful of Vengeance
MexZombies
Persuasion
Hot Seat
The Contractor
On the Line
All the Beauty and the Bloodshed
The Seven Deadly Sins: Grudge of Edinburgh Part 1
The OctoGames
One Week Friends
Athena
Hellhole
Attack on Titan
Umma
The Bob's Burgers Movie
Spitfire Over Berlin
The First Slam Dunk
The Goldsmith
The Northman
Attack
All Quiet on the Western Front
Reign of Chaos
Prey
Marry Me
The Princess
Viking Wolf
Shin Ultraman
Selena Gomez: My Mind & Me
New Gods: Yang Jian
Clean
Blue's Big City Adventure
Night at the Museum: Kahmunrah Rises Again
Men
Shotgun Wedding
Sharp Stick
Jerry & Marge Go Large
Day Shift
365 Days: This Day
The Cellar
Scream
Abandoned
Vesper
After the Pandemic
Turning Red
Princess Cursed in Time: Alchemist's Quest
Paradise City
Out of the Blue
Father Stu
Teddy's Christmas
Sisu
TÁR
White Noise
The Ice Age Adventures of Buck Wild
Halloween Ends
The Exorcism of Hannah Stevenson
Guillermo del Toro's Pinocchio
One Piece Film Red
Roald Dahl's Matilda the Musical
The Infernal Machine
Doraemon the Movie: Nobita's Little Star Wars 2021
Thirteen Lives
Harry Potter 20th Anniversary: Return to Hogwarts
Blade of the 47 Ronin
Jana Gana Mana
Mortal Kombat Legends: Snow Blind
Inspector Sun and the Curse of the Black Widow
Patriots Time
Jurassic World Dominion
Buried in Barstow
Hunt
Fortress: Sniper's Eye
Suzume
Top Gunner: Danger Zone
Me Time
Fullmetal Alchemist: The Final Alchemy
Aftersun
Vicini di casa
The Witch: Part 2. The Other One
Breaking
Super Furball Saves the Future
The Mystic Nine: Begonia from Qingshan
Jurassic World Camp Cretaceous: Hidden Adventure
The Outfit
Silip Sa Apoy
Murder at Yellowstone City
The Batman
Amsterdam
Panama
Magnum Opus
The System
Against the Ice
Beast
The Whale
Virgin Forest
Smile
Sonic the Hedgehog 2
Redeeming Love
Incantation
Mr. Harrigan's Phone
Terrifier 2
The House
Death on the Nile
The Survivor
13 Exorcisms
Hotel Transylvania: Transformania
Feed
Orphan: First Kill
Hellraiser
Emily the Criminal
RRR
Bodies Bodies Bodies
Pinocchio
The Bad Guys
I Came By
Slumberland
Batman and Superman: Battle of the Super Sons
The Jack in the Box: Awakening
The Pirates: The Last Royal Treasure
Dangerous Liaisons
Finnick
Operation Seawolf
Section 8
Purple Hearts
The King's Daughter
Disenchanted
Sword Art Online the Movie – Progressive – Scherzo of Deep Night
The Royal Treatment
Good Luck to You, Leo Grande
Sick
Carter
Kimi
Brazen
Dead for a Dollar
Raiders of the Lost Library
Shark Side of the Moon
Morbius
Watcher
Apollo 10½: A Space Age Childhood
Survive
Sex Appeal
Downton Abbey: A New Era
Forever Out of My League
Emergency Declaration
Don't Worry Darling
Devotion
The Big 4
Troll
That Time I Got Reincarnated as a Slime the Movie: Scarlet Bond
Crush
Black Crab
The Invitation
Last Looks
Slash/Back
Project Wolf Hunting
Staring at Strangers
Living
The Last 10 Years
The In Between
Everything Everywhere All at Once
V/H/S/99
Rise of the Teenage Mutant Ninja Turtles: The Movie
Black Panther: Wakanda Forever
All Eyes Off Me
Broker
The Munsters
Deep Water
All the Old Knives
Battle for the Western Front
Grimcutty
Accident Man: Hitman's Holiday
The Soccer Football Movie
Fireheart
Black Site
Spider-Man: All Roads Lead to No Way Home
Barbarian
Luck
Werewolf by Night
Strange World
Gold
Puss in Boots: The Last Wish
Bones and All
Paws of Fury: The Legend of Hank
The Wrath of God
Enola Holmes 2
Avatar: The Way of Water
Virus:32
Shark Bait
The Exorcism of God
Play Dead
Constantine: The House of Mystery
Detective Conan: The Bride of Halloween
They/Them
Hansan: Rising Dragon
The Fabelmans
Bullet Train Down
Beyond the Universe
Obi-Wan Kenobi: A Jedi's Return
See How They Run
Diary of a Wimpy Kid: Rodrick Rules
Violent Night
Groot's First Steps
The Railway Children Return
Sniper: Rogue Mission
The Legend of La Llorona
Honeymoon with My Mother
God's Crooked Lines
Doctor Strange in the Multiverse of Madness
The Boss Baby: Christmas Bonus
Sniper: The White Raven
The Lake
A Man Called Otto
The Gray Man
The Valet
Nanny
The Quintessential Quintuplets Movie
Fresh
2037
The Voyagers
Chip 'n Dale: Rescue Rangers
Dragon Ball Super: Super Hero
Shadow Master
The Banshees of Inisherin
Jackass Forever
Project Gemini
Bros
Teen Titans Go! & DC Super Hero Girls: Mayhem in the Multiverse
Wifelike
The Sea Beast
Whitney Houston: I Wanna Dance with Somebody
Groot Takes a Bath
The Trapped 13: How We Survived The Thai Cave
Thor: Love and Thunder
Catwoman: Hunted
Sri Asih
The Ancestral
Run, Tiger Run!
The Lost City
9 Bullets
Elvis
Glass Onion: A Knives Out Mystery
The Reef: Stalked
Good Boy
Lamborghini: The Man Behind the Legend
The Simpsons Meet the Bocellis in "Feliz Navidad"
The Son
Tom and Jerry: Snowman's Land
Senior Year
Sonic Drone Home
High & Low The Worst X
Tom and Jerry Cowboy Up!
Top Gun: Maverick
Close
Jackass 4.5
Outsource
Samaritan
Lightyear
The Roundup
Minions: The Rise of Gru
Ambulance
Triangle of Sadness
Armageddon Time
The Guardians of the Galaxy Holiday Special
The Next 365 Days
Avatar: The Deep Dive - A Special Edition of 20/20
Till
20th Century Girl
578: Magnum
Medieval
My Name Is Vendetta
The Tunnel to Summer, the Exit of Goodbyes
Blonde
The Battle at Lake Changjin II: Water Gate Bridge
Agent Game
Lou
Home Team
The Greatest Beer Run Ever
Z-O-M-B-I-E-S 3
Lady Chatterley's Lover
Hatching
Vikram
After Ever Happy
Vengeance
The Unbearable Weight of Massive Talent
To Me, the One Who Loved You
The Lost Lotteries
Deep Sea Mutant Snake
Dog
Honor Society
Welcome to the Club
Fall
Overdose
The 355
Emancipation
Do Revenge
Decision to Leave
Mindcage
Teasing Master Takagi-san: The Movie
A Day to Die
My Grandfather's Demons
Lesson Plan
Love and Leashes
Last Seen Alive
Sex Game 6969
The Adam Project
Operation Mincemeat
Warriors of Future
Wyrmwood: Apocalypse
Superwho?
Vendetta
Infinite Storm
No Exit
Dampyr
Spiderhead
The Last Manhunt
Yaksha: Ruthless Operations
The Adventure of A.R.I.: My Robot Friend
The Woman King
Ticket to Paradise
The Enforcer
Blacklight
Love Like the Falling Petals
Nope
Galaxy Games
Three Thousand Years of Longing
The Menu
Prey for the Devil
Frank and Penelope
Where the Crawdads Sing
Interceptor
Lyle, Lyle, Crocodile
The Offering
The House
Blood and Snow
The Family Plan
Godzilla Minus One
Beautiful Disaster
The Animal Kingdom
Sleep
Unlocked
Asterix & Obelix: The Middle Kingdom
Clock
Guy Ritchie's The Covenant
Zom 100: Bucket List of the Dead
Faraway
Murder Mystery 2
Ghosted
The Hill
Cheat
BlackBerry
Robot Dreams
In Your Dreams
Craig Before the Creek
Dear David
Love at First Sight
The Arctic Convoy
The Secret Kingdom
Shin Kamen Rider
Poison
The Iron Claw
Shazam! Fury of the Gods
Kill Shot
The Inseparables
The Concierge
Hard Feelings
Skinamarink
Deep Fear
Magic Mike's Last Dance
Believer 2
There's Still Tomorrow
Missing
Sister Death
Aquaman and the Lost Kingdom
Extraction 2
Justice League: Warworld
Ambush
Brooklyn 45
God Is a Bullet
¿Quieres ser mi hijo?
Call Me Chihiro
Dashing Through the Snow
Joy Ride
Freud's Last Session
Concrete Utopia
Tin & Tina
Once Upon a Studio
Plane
Last Sentinel
Leave the World Behind
80 for Brady
12th Fail
Eileen
The Flood
Sniper: G.R.I.T. - Global Response & Intelligence Team
Elevator Game
The Rat Catcher
Mars Express
Role Play
maboroshi
The Tank
Bottoms
Salaar: Part 1 - Ceasefire
The Ritual Killer
You're Killing Me
The Canterville Ghost
The War of the Nonni
The Monkey King
Ruby Gillman, Teenage Kraken
Family Switch
The Devil Conspiracy
SPY x FAMILY CODE: White
Bird Box Barcelona
Reality
The Coffee Table
No One Will Save You
Deliver Us
Death Whisperer
Expend4bles
Peter Pan & Wendy
Kisi Ka Bhai... Kisi Ki Jaan
Mr. Monk's Last Case: A Monk Movie
Trunk - Locked In
Batman: The Doom That Came to Gotham
All-Time High
Maestro
Barbie
Brave Citizen
The Mother
Beau Is Afraid
A Million Miles Away
The Communion Girl
Forgotten Love
The Other Zoey
Shooting Stars
Totally Killer
Make Me Believe
Johnny & Clyde
Operation Napoleon
Freddy's Fridays
57 Seconds
Anyone But You
The Teachers' Lounge
Perfect Days
Nowhere
Killer Book Club
Are You There God? It's Me, Margaret.
War of the Worlds: The Attack
Saw X
The Childe
Talk to Me
Black Clover: Sword of the Wizard King
A Haunting in Venice
The Sexologist
Creed III
The Squad: Home Run
Ballerina
Fear the Night
Sasaki and Miyano: Graduation
Fast Charlie
Rascal Does Not Dream of a Sister Venturing Out
Silent Night
The Nun II
Exorcism in Utero
It Lives Inside
Black Noise
Desperation Road
The X-Treme Riders
Boudica
65
Little Bone Lodge
When Evil Lurks
Wingwomen
The Zone of Interest
Guardians of the Galaxy Vol. 3
The Tower
Making Squid Game: The Challenge
Strays
Sound of Freedom
Creation of the Gods I: Kingdom of Storms
Alice in Terrorland
La Leyenda de los Chaneques
Haunted Mansion
The Wandering Earth II
Resident Evil: Death Island
Finestkind
V/H/S/85
Legion of Super-Heroes
Lassie: A New Adventure
Miraculous World: Paris, Tales of Shadybug and Claw Noir
To Catch a Killer
Coup de Chance
The Killer
In the Land of Saints and Sinners
The Equalizer 3
172 Days
Mission: Impossible - Dead Reckoning Part One
The Hunger Games: The Ballad of Songbirds & Snakes
American Fiction
Scream VI
The Bad Guys: A Very Bad Holiday
3 Days in Malay
Cursed
Radical
Ape vs Mecha Ape
The Exorcist: Believer
The Last Kingdom: Seven Kings Must Die
El Conde
The Retirement Plan
Through My Window: Across the Sea
Consent
Transfusion
Monster
Inside
Saltburn
Air
How to Have Sex
Hell House LLC Origins: The Carmichael Manor
The Smoke Master
The Holdovers
Ant-Man and the Wasp: Quantumania
Diary of a Wimpy Kid Christmas: Cabin Fever
Due Justice
Muzzle
Meg 2: The Trench
Operation Fortune: Ruse de Guerre
Freelance
My Fault
Passages
Insidious: The Red Door
Chicken Run: Dawn of the Nugget
The Adventures
Cobweb
Hidden Strike
Crater
Thanksgiving
Spy Kids: Armageddon
Monster High 2
The Five
Butcher's Crossing
Baghead
Evil Dead Rise
Soulmate
Puppy Love
Battle Over Britain
Mob Land
The Island
3 Days Max
Gods of the Deep
The Pig, the Snake and the Pigeon
15 Cameras
Shrapnel
Skal: Fight for Survival
Sakra
The Out-Laws
You Are So Not Invited to My Bat Mitzvah
Out of Exile
The Pope's Exorcist
The Last Voyage of the Demeter
Rustin
White Bird
Napoleon
Oppenheimer
Rebel Moon - Part One: A Child of Fire
Dogman
Black Lotus
Dora: Say Hola to Adventure!
Kill Boksoon
Night of the Hunted
Evil Does Not Exist
The Kill Room
Last Summer
Wish
A Thousand and One
Miraculous: Ladybug & Cat Noir, The Movie
Priscilla
Teen Wolf: The Movie
Jawan
Fear
The Boy and the Heron
Indiana Jones and the Dial of Destiny
Money Shot: The Pornhub Story
The Conference
Five Nights at Freddy's
The Flash
LEGO Marvel Avengers: Code Red
Mercy
The Black Demon
Chupa
La Chimera
No Hard Feelings
There's Something Wrong with the Children
Underground Monster
Knights of the Zodiac
Ruthless
Anatomy of a Fall
The Monk and the Gun
Red Rooms
Reptile
The Boogeyman
John Wick: Chapter 4
Kingdom III: The Flame of Destiny
Retribution
The Magician's Elephant
Cocaine Bear
The Seven Deadly Sins: Grudge of Edinburgh Part 2
A Good Person
Paradise
Infested
Transformers: Rise of the Beasts
After Everything
The Little Mermaid
Flamin' Hot
Asteroid City
Perfect Addiction
Renfield
Shimmy: The First Monkey King
The Bricklayer
Heart of Stone
Mayhem!
Adipurush
Memory
Totto-chan: The Little Girl at the Window
Knock at the Cabin
Rascal Does Not Dream of a Knapsack Kid
Mavka: The Forest Song
The Tutor
Vikings: Battle of Heirs
Die Hart
Smugglers
Red, White & Royal Blue
Sanctuary
Boonie Bears: Guardian Code
The Devil Comes at Night
Warhorse One
The Girl Who Escaped: The Kara Robinson Story
Kandahar
Diabolik - Who Are You?
Carl's Date
Operation Black Ops
Nefarious
Confidential Informant
The Marvels
See You on Venus
Heaven in Hell
The Three Musketeers: Milady
Hunt Club
Rally Road Racers
Mummies
Elemental
Detective Conan: Black Iron Submarine
All of Us Strangers
Hachiko
Spider-Man: Across the Spider-Verse
Let Her Kill You
The Rage
Dumb Money
The Three Musketeers: D'Artagnan
Pet Sematary: Bloodlines
Killers of the Flower Moon
Past Lives
Sly
Society of the Snow
The Creator
Soulcatcher
Ferrari
All the Names of God
Woman in the Maze
The Abyss
Winnie the Pooh: Blood and Honey
Blue Beetle
Blood & Gold
Nimona
Supercell
Animal
Gran Turismo
Io Capitano
Teenage Mutant Ninja Turtles: Mutant Mayhem
Mantra Warrior: The Legend of The Eight Moons
Hunting Games
The Roundup: No Way Out
South Park: Joining the Panderverse
The Quest for Tom Sawyer's Gold
The Super Mario Bros. Movie
Dungeons & Dragons: Honor Among Thieves
Transmorphers: Mech Beasts
Rule 34
The Peasants
The Velveteen Rabbit
Acid
Ride On
Wonka
May December
Tetris
Burning Betrayal
PAW Patrol: The Mighty Movie
Leo
Dream Scenario
AKA
Abbé Pierre - A Century of Devotion
Mortal Kombat Legends: Cage Match
Hunger
The Promised Land
The Color Purple
The Deep Dark
No More Bets
Merry Little Batman
Poor Things
Big George Foreman
Sharper
Migration
Pain Hustlers
Fast X
Deep Sea
One Life
Tayuan
Darkland: The Return
The Wrath of Becky
Noryang: Deadly Sea
The Pod Generation
Love in Taipei
Infinity Pool
Monolith
The Mummy Resurrection
Hypnotic
King of Killers
Trolls Band Together
The Taste of Things
MR-9: Do or Die
Marguerite's Theorem
The Wonderful Story of Henry Sugar
The Collective
Naughty
TAYLOR SWIFT | THE ERAS TOUR
Queer
Darkness of Man
The Last Breath
The Mouse Trap
Conclave
Ultraman: Rising
Prey
Inside Out 2
Megamind vs. the Doom Syndicate
The Wages of Fear
Drawing Closer
Rebel Ridge
Sex
In a Violent Nature
The Room Next Door
Look Back
May the 12th Be with You
The Red Virgin
Pimpinero: Blood and Oil
Unstoppable
Sting
Outlaw
The Beekeeper
Harold and the Purple Crayon
Ghostbusters: Frozen Empire
Miraculous World, London: At the Edge of Time
Beetlejuice Beetlejuice
Love Lies Bleeding
Caddo Lake
Blood for Dust
The Garfield Movie
Sumala
Wicked
Pushpa 2 - The Rule
Absolution
Transformers One
Bogotá: City of the Lost
Boneyard
The Exorcism
OVERLORD: The Sacred Kingdom
Snoopy Presents: Welcome Home, Franklin
The Killer's Game
Paddington in Peru
The Courier
Armor
Woman of the Hour
Nickel Boys
Time Cut
The Jack in the Box Rises
The Apprentice
The Wild Robot
The Last Kumite
Emilia Pérez
Kill 'em All 2
SpongeBob Squarepants: Kreepaway Kamp
Golden Kamuy
Love in 39 Degrees
Temurun
Watchmen: Chapter II
The Soul Eater
Baki Hanma VS Kengan Ashura
Attack on Titan: THE LAST ATTACK
Godzilla x Kong: The New Empire
The Clean Up Crew
Megalopolis
Ordinary Angels
Hard Truths
Hit Man
Moana 2
The Return
Undercover
The Ministry of Ungentlemanly Warfare
Reagan
Squid Game: Fireplace
Kraven the Hunter
Madame Web
Horizon: An American Saga - Chapter 1
Jugaremos en el bosque
Hijack 1971
Classified
Something in the Water
Hidden Face
Kneecap
The 47
Miller's Girl
Invasion
Twisters
It Ends with Us
Disaster Holiday
Saturday Night's Main Event XXXVII
Project Silence
Red One
The Simpsons: O C'mon All Ye Faithful
Brothers
The Lord of the Rings: The War of the Rohirrim
We Are Zombies
Trap
Blink Twice
Code 8 Part II
Y2K
MaXXXine
The Killer
Monkey Man
How to Make Millions Before Grandma Dies
Kalki 2898-AD
Rebel Moon - Part Two: The Scargiver
White Snake: Afloat
Werewolves
Gladiator II
Late Night with the Devil
Fly Me to the Moon
The Instigators
Beezel
Non Negotiable
Civil War
1992
Breathe
The Lost Children
Speak No Evil
Elyas
Mission: Cross
Night Call
V/H/S/Beyond
Emmanuelle
Eye for an Eye 2
No Time to Spy: A Loud House Movie
Amityville: Where the Echo Lives
Joni pata de conejo
The Seed of the Sacred Fig
The Count of Monte Cristo
The Forge
The Demon Disorder
Levels
The Shadow Strays
Arena Wars
Woody Woodpecker Goes to Camp
Uprising
Let Go
Parthenope
A Different Man
Wake Up
BLUE LOCK THE MOVIE -EPISODE NAGI-
Longlegs
The Bad Shepherd
Old Guy
Buffalo Kids
House of Ga'a
Deadpool & Wolverine
The Crow
The Price of Money: A Largo Winch Adventure
No Voltees
Duchess
IF
Night Swim
Salve Maria
The Tearsmith
Bagman
Kung Fu Panda 4
The King Tide
Incoming
The Silent Hour
One Fast Move
Marco
Road House
The Beast
Bureau 749
Kingdom of the Planet of the Apes
Canary Black
Babygirl
Spaceman
Oddity
Your Fault
Small Things Like These
Maria
Apocalypse Z: The Beginning of the End
Niko: Beyond the Northern Lights
Dear Santa
Here
The Ballad of Davy Crockett
Case Closed: The Million-Dollar Pentagram
Meeting with Pol Pot
Trigger Warning
The Order
Sing: Thriller
The Front Room
Cinderella's Curse
El Candidato Honesto
City Hunter
The Haunting of the Immaculate Nurse
Nightbitch
Land of Bad
Sixty Minutes
Bad Boys: Ride or Die
Uglies
2073
Smile 2
Better Man
Dirty Angels
Lazareth
September 5
Knox Goes Away
Last Stop: Rocafort St.
Lee
No Way Up
House of Spoils
The Idea of You
The Tiger's Apprentice
This Time Next Year
Devara: Part 1
The Roundup: Punishment
Die Alone
Orion and the Dark
Challengers
Do Not Enter
The Watchers
LEGO Marvel Avengers: Mission Demolition
Zak & Wowo: The Legend of Lendarys
Crayon Shin-chan the Movie: Our Dinosaur Diary
My Old Ass
A Family Affair
Carry-On
Justice League: Crisis on Infinite Earths Part Two
Whisper of the Witch
The Strangers: Chapter 1
Twilight of the Warriors: Walled In
Hounds of War
Ask Me What You Want
Argylle
The Fall Guy
An Almost Christmas Story
Aftermath
Anaconda
The Aunts
Solo Leveling -ReAwakening-
Escape from the 21st Century
The Day the Earth Blew Up: A Looney Tunes Movie
Nosferatu
Gunner
Take Cover
Cash Out
Alien: Romulus
Kung Fu Games
The Wailing
GTMAX
Freedom
Paradox Effect
Atlas
Imaginary
MadS
Abigail
Lift
Joker: Folie à Deux
Rita
The Girl with the Needle
The Devil's Bath
Skinford 2: The Curse
Elevation
Tarot
My Hero Academia: You're Next
Dead Talents Society
Autumn and the Black Jaguar
Immaculate
Mean Girls
Saturday Night
Dominique
The Substance
Lost Ladies
A Complete Unknown
Snack Shack
SDI: Secret Department of Intervention
The Deliverance
Thelma
Mufasa: The Lion King
The Calendar Killer
Gracie & Pedro: Pets to the Rescue
Double Blind
Number 24
Sonic the Hedgehog 3
Spellbound
Carved
Sikandar Ka Muqaddar
The Outrun
The Moor
Thelma the Unicorn
Borderlands
Blitz
Wallace & Gromit: Vengeance Most Fowl
Death Whisperer 2
Terrifier 3
Through My Window 3: Looking at You
Furiosa: A Mad Max Saga
Noah's Ark
Dune: Part Two
The Bad Guys: Haunted Heist
Badland Hunters
Chief of Station
HAIKYU!! The Dumpster Battle
Never Let Go
Justice League: Crisis on Infinite Earths Part One
The Brutalist
Don't Move
Animal Adventures: Save The Forest
Latency
Strange Darling
The Thundermans Return
The Six Triple Eight
Boy Kills World
The Prosecutor
Panda Plan
Bionic
Winnie-the-Pooh: Blood and Honey 2
Azrael
Hunting With Tigers
Drive-Away Dolls
Arcadian
The Bikeriders
Outside
Under Paris
Descendants: The Rise of Red
Young Hearts
That Christmas
Dead Sea
Kingdom IV: Return of the Great General
Damaged
Homestead
Despicable Me 4
Damsel
Beverly Hills Cop: Axel F
Cellar Door
Upgraded
The Green Deal
Jackpot!
South Park: The End of Obesity
The Sand Castle
The Platform 2
Exhuma
Beating Hearts
Trouble
My Spy The Eternal City
Rippy
It's What's Inside
Venom: The Last Dance
Remnant
The Thicket
Odio el verano
Afraid
The Well
I'm Still Here
Big City Greens the Movie: Spacecation
Kalikot
Memoir of a Snail
Mr. Crocket
The Beast Within
The Last Showgirl
Bangkok Breaking: Heaven and Hell
Kinds of Kindness
Anora
Greedy People
The Children's Train
Handling the Undead
A Real Pain
Black Tea
Kleks Academy
Kill
Apartment 7A
Dragonkeeper
Am I OK?
Distant
Family Pack
10 Lives
Ferry 2
River of Blood
Sleeping Dogs
Amaran
Officer Black Belt
I Saw the TV Glow
Saving Bikini Bottom: The Sandy Cheeks Movie
Mickey and the Very Many Christmases
Hellboy: The Crooked Man
Salem's Lot
Humane
Bag of Lies
Big World
Alienoid: Return to the Future
The Convert
We Live in Time
The Absence of Eden
Survive
Weekend in Taipei
Daddy's Head
Cuckoo
A Quiet Place: Day One
Flow
Arthur the King
The Union
The First Omen
Someone Like You
Monster Summer
Juror #2
Wolfs

1.1.3 Combine All Movie IDs with Titles

import pandas as pd

# Read the CSV files
ids_df = pd.read_csv('/Users/heshuqing0305/Downloads/movie_all_ids_2014_2024_tmdb.csv')
titles_df = pd.read_csv('/Users/heshuqing0305/Downloads/movie_all_titles_2014_2024_tmdb.csv')

# Concatenate the two DataFrames along the columns
merged_df = pd.concat([ids_df, titles_df], axis=1)

# Save the merged result to a new CSV file
merged_df.to_csv('merged_all_movie_data_2014_2024.csv', index=False, encoding='utf-8-sig')

print("Merge complete, saved as 'merged_all_movie_data_2014_2024.csv'")

Merge complete, saved as 'merged_all_movie_data_2014_2024.csv'

1.1.4 Request All Movie Data

from imdb import Cinemagoer
import pandas as pd
import time

# Create a Cinema object
cinema = Cinemagoer()

# Read the CSV file
try:
    movie_titles_df = pd.read_csv('/Users/heshuqing0305/Downloads/merged_all_movie_data_2014_2024.csv')  # Replace with the actual path
    print("CSV file loaded successfully.")
except Exception as e:
    print(f"Error loading CSV file: {e}")
    exit()

# Define the range of years you want to process
years_to_request = range(2014, 2024)  # From 2014 to 2024

# Create a new list to store movie information
movie_info_list = []

# Iterate through each year
for year_to_request in years_to_request:
    print(f"Processing movies from the year {year_to_request}...")
    
    if 'Year' in movie_titles_df.columns and 'Movie Title' in movie_titles_df.columns:
        year_movies = movie_titles_df[movie_titles_df['Year'] == year_to_request]
        print(f"Found {len(year_movies)} movies from the year {year_to_request}.")
    else:
        print("CSV file does not contain 'Year' or 'Movie Title' columns.")
        exit()

    # Get the total number of movies to process for the current year
    total_movies = len(year_movies)

    # Iterate through each movie for the current year
    for index, title in enumerate(year_movies['Movie Title']):
        print(f"Processing movie {index + 1}/{total_movies}: {title}")  # Display current progress
        try:
            movies = cinema.search_movie(title)
            if movies:
                movie = movies[0]  # Get the first movie from the search results
                cinema.update(movie)  # Update the movie information to get complete data

                # Collect movie information
                movie_info = {
                    'Title': movie.get('title', 'N/A'),
                    'Year': movie.get('year', 'N/A'),
                    'IMDb ID': movie.movieID,  # Get IMDb ID
                    'Director': ', '.join([director['name'] for director in movie.get('directors', [])]) or 'N/A',
                    'Rating': movie.get('rating', 'N/A'),  # IMDb rating
                    'Votes': movie.get('votes', 'N/A'),  # Number of votes
                    'Box Office': movie.get('box office', {}).get('Cumulative Worldwide Gross', 'N/A'),
                    'Budget': movie.get('box office', {}).get('Budget', 'N/A'),# Global box office
                    'Genres': ', '.join(movie.get('genres', [])) or 'N/A',  # Get movie genres
                    'Actors': ', '.join([actor['name'] for actor in movie.get('cast', [])[:10]]) or 'N/A',  # Get top 10 actors
                    'Languages': ', '.join(movie.get('languages', [])) or 'N/A',  # Get movie languages
                    'Countries': ', '.join(movie.get('countries', [])) or 'N/A'  # Get production countries/regions
                }
                movie_info_list.append(movie_info)
            else:
                print(f"No movie found for title: {title}")
        except Exception as e:
            print(f"Error processing movie '{title}': {e}")
        
        # Delay for two seconds to avoid being rate-limited
        time.sleep(2)

# Convert the results to a DataFrame
movie_info_df = pd.DataFrame(movie_info_list)

# Optionally, you can save the DataFrame to a CSV file
movie_info_df.to_csv('merged_all_movies.csv.csv', index=False, encoding='utf-8-sig')
print("Movie information has been saved to 'merged_all_movies.csv.csv'")

CSV file loaded successfully.
Processing movies from the year 2014...
Found 400 movies from the year 2014.
Processing movie 1/400: X-Men: Days of Future Past
Processing movie 2/400: If I Stay
Processing movie 3/400: Leviathan
Processing movie 4/400: The Purge: Anarchy
Processing movie 5/400: Adult World
Processing movie 6/400: Batman: Assault on Arkham
Processing movie 7/400: Get on Up
Processing movie 8/400: Doraemon: New Nobita's Great Demon - Peko and the Exploration Party of Five
Processing movie 9/400: Godzilla
Processing movie 10/400: Gun Woman
Processing movie 11/400: The Town that Dreaded Sundown
Processing movie 12/400: Dolphin Tale 2
Processing movie 13/400: What We Did on Our Holiday
Processing movie 14/400: Transcendence
Processing movie 15/400: Zombie Fight Club
Processing movie 16/400: Kill the Messenger
Processing movie 17/400: The Salvation
Processing movie 18/400: Birdman or (The Unexpected Virtue of Ignorance)
Processing movie 19/400: The Captive
Processing movie 20/400: Alexander and the Terrible, Horrible, No Good, Very Bad Day
Processing movie 21/400: Beyond the Reach
Processing movie 22/400: Whiplash
Processing movie 23/400: About Last Night
Processing movie 24/400: The Boxtrolls
Processing movie 25/400: Before I Go to Sleep
Processing movie 26/400: That Awkward Moment
Processing movie 27/400: Spring
Processing movie 28/400: Jacky in the Kingdom of Women
Processing movie 29/400: A Pigeon Sat on a Branch Reflecting on Existence
Processing movie 30/400: Skin Trade
Processing movie 31/400: The Interview
Processing movie 32/400: The Lego Movie
Processing movie 33/400: Scooby-Doo! WrestleMania Mystery
Processing movie 34/400: The Other Woman
Processing movie 35/400: Pride
Processing movie 36/400: The Quiet Ones
Processing movie 37/400: Calvary
Processing movie 38/400: Two Night Stand
Processing movie 39/400: I, Frankenstein
Processing movie 40/400: [REC]⁴ Apocalypse
Processing movie 41/400: Road to Paloma
Processing movie 42/400: The Snow Queen 2: Refreeze
Processing movie 43/400: Alien Abduction
Processing movie 44/400: In Your Eyes
Processing movie 45/400: Guardians of the Galaxy
Processing movie 46/400: Indigenous
Processing movie 47/400: The Imitation Game
Processing movie 48/400: I Origins
Processing movie 49/400: The Remaining
Processing movie 50/400: Still Alice
Processing movie 51/400: A Long Way Down
Processing movie 52/400: Teenage Mutant Ninja Turtles
Processing movie 53/400: The Normal Heart
Processing movie 54/400: Messi
Processing movie 55/400: Lupin the 3rd
Processing movie 56/400: Joe
Processing movie 57/400: The Third One
Processing movie 58/400: Night Moves
Processing movie 59/400: Stand by Me Doraemon
Processing movie 60/400: The Signal
Processing movie 61/400: Love, Rosie
Processing movie 62/400: Maleficent
Processing movie 63/400: Hercules
Processing movie 64/400: Annabelle
Processing movie 65/400: Million Dollar Arm
Processing movie 66/400: Selma
Processing movie 67/400: Chef
Processing movie 68/400: Dawn of the Planet of the Apes
Processing movie 69/400: Blue Ruin
Processing movie 70/400: Halo: Nightfall
Processing movie 71/400: When the Game Stands Tall
Processing movie 72/400: Dead Snow 2: Red vs. Dead
Processing movie 73/400: No Tears for the Dead
Processing movie 74/400: Deliver Us from Evil
Processing movie 75/400: Sub Rosa
Processing movie 76/400: Wild
Processing movie 77/400: A Girl Walks Home Alone at Night
Processing movie 78/400: The Water Diviner
Processing movie 79/400: Annie
Processing movie 80/400: Where Hope Grows
Processing movie 81/400: Big Hero 6
Processing movie 82/400: Barbie: The Pearl Princess
Processing movie 83/400: The Fault in Our Stars
Processing movie 84/400: Maya the Bee Movie
Processing movie 85/400: Shrew's Nest
Processing movie 86/400: Phoenix
Processing movie 87/400: Babysitting
Processing movie 88/400: The One I Love
Processing movie 89/400: Night at the Museum: Secret of the Tomb
Processing movie 90/400: Seal Team Eight: Behind Enemy Lines
Processing movie 91/400: Top Five
Processing movie 92/400: The Torture Club
Processing movie 93/400: Dragon Nest: Warriors' Dawn
Processing movie 94/400: Exodus: Gods and Kings
Processing movie 95/400: 300: Rise of an Empire
Processing movie 96/400: Hector and the Search for Happiness
Processing movie 97/400: The November Man
Processing movie 98/400: Yves Saint Laurent
Processing movie 99/400: Third Person
Processing movie 100/400: Crayon Shin-chan: Intense Battle! Robo Dad Strikes Back
No movie found for title: Crayon Shin-chan: Intense Battle! Robo Dad Strikes Back
Processing movie 101/400: American Sniper
Processing movie 102/400: How to Train Your Dragon 2
Processing movie 103/400: Jai Ho
Processing movie 104/400: Locke
Processing movie 105/400: 3 Days to Kill
Processing movie 106/400: The Hunger Games: Mockingjay - Part 1
Processing movie 107/400: '71
Processing movie 108/400: Detective Conan: Dimensional Sniper
Processing movie 109/400: Starry Eyes
Processing movie 110/400: Divergent
Processing movie 111/400: Effie Gray
Processing movie 112/400: Northmen: A Viking Saga
Processing movie 113/400: Sin City: A Dame to Kill For
Processing movie 114/400: Black Sea
Processing movie 115/400: The Expendables 3
Processing movie 116/400: Mr. Peabody & Sherman
Processing movie 117/400: The Connection
Processing movie 118/400: Taken 3
Processing movie 119/400: Song of the Sea
Processing movie 120/400: The Little Death
Processing movie 121/400: Paranormal Activity: The Marked Ones
Processing movie 122/400: The Bélier Family
Processing movie 123/400: In the Blood
Processing movie 124/400: Ride
Processing movie 125/400: Pompeii
Processing movie 126/400: Rise of the Legend
Processing movie 127/400: Monster High: Freaky Fusion
Processing movie 128/400: The Frame
Processing movie 129/400: Behaving Badly
Processing movie 130/400: Haunt
Processing movie 131/400: Brick Mansions
Processing movie 132/400: The Dead Lands
Processing movie 133/400: Enemy
Processing movie 134/400: Under the Skin
Processing movie 135/400: The Monkey King
Processing movie 136/400: The Hobbit: The Battle of the Five Armies
Processing movie 137/400: Edge of Tomorrow
Processing movie 138/400: Ask Me Anything
Processing movie 139/400: For a Handful of Kisses
Processing movie 140/400: The Pirates
Processing movie 141/400: The Admiral: Roaring Currents
Processing movie 142/400: Nightcrawler
Processing movie 143/400: GirlHouse
Processing movie 144/400: Jessabelle
Processing movie 145/400: The Angriest Man in Brooklyn
Processing movie 146/400: Into the Woods
Processing movie 147/400: You're Not You
Processing movie 148/400: Petals on the Wind
Processing movie 149/400: A Girl at My Door
Processing movie 150/400: Flowers in the Attic
Processing movie 151/400: What We Do in the Shadows
Processing movie 152/400: The Amazing Spider-Man 2
Processing movie 153/400: 22 Jump Street
Processing movie 154/400: The Drop
Processing movie 155/400: Tamako Love Story
Processing movie 156/400: The White Haired Witch of Lunar Kingdom
Processing movie 157/400: Creep
Processing movie 158/400: Son of God
Processing movie 159/400: Serial (Bad) Weddings
Processing movie 160/400: Trash
Processing movie 161/400: No Good Deed
Processing movie 162/400: Camp X-Ray
Processing movie 163/400: Endless Love
Processing movie 164/400: E N V O Y
Processing movie 165/400: Iceman
Processing movie 166/400: Moms' Night Out
Processing movie 167/400: The Gambler
Processing movie 168/400: The Science of Interstellar
Processing movie 169/400: Draft Day
Processing movie 170/400: Justice League: War
Processing movie 171/400: Planes: Fire & Rescue
Processing movie 172/400: The Rewrite
Processing movie 173/400: Inherent Vice
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Processing movie 229/400: Boyhood

2025-03-02 20:56:13,681 CRITICAL [imdbpy] /Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/imdb/_exceptions.py:32: IMDbDataAccessError exception raised; args: ({'errcode': None, 'errmsg': 'None', 'url': 'https://www.imdb.com/title/tt1065073/reference', 'proxy': '', 'exception type': 'IOError', 'original exception': TimeoutError('The read operation timed out')},); kwds: {}
Traceback (most recent call last):
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/imdb/parser/http/__init__.py", line 234, in retrieve_unicode
    content = response.read()
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/http/client.py", line 459, in read
    return self._read_chunked(amt)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/http/client.py", line 591, in _read_chunked
    value.append(self._safe_read(chunk_left))
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/http/client.py", line 630, in _safe_read
    data = self.fp.read(amt)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/socket.py", line 705, in readinto
    return self._sock.recv_into(b)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/ssl.py", line 1274, in recv_into
    return self.read(nbytes, buffer)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/ssl.py", line 1130, in read
    return self._sslobj.read(len, buffer)
TimeoutError: The read operation timed out
2025-03-02 20:56:13,692 CRITICAL [imdbpy] /Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/imdb/__init__.py:850: caught an exception retrieving or parsing "main" info set for mopID "1065073" (accessSystem: http)
Traceback (most recent call last):
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/imdb/parser/http/__init__.py", line 234, in retrieve_unicode
    content = response.read()
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/http/client.py", line 459, in read
    return self._read_chunked(amt)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/http/client.py", line 591, in _read_chunked
    value.append(self._safe_read(chunk_left))
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/http/client.py", line 630, in _safe_read
    data = self.fp.read(amt)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/socket.py", line 705, in readinto
    return self._sock.recv_into(b)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/ssl.py", line 1274, in recv_into
    return self.read(nbytes, buffer)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/ssl.py", line 1130, in read
    return self._sslobj.read(len, buffer)
TimeoutError: The read operation timed out

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/imdb/__init__.py", line 848, in update
    ret = method(mopID)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/imdb/parser/http/__init__.py", line 481, in get_movie_main
    cont = self._retrieve(self.urls['movie_main'] % movieID + 'reference')
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/imdb/parser/http/__init__.py", line 392, in _retrieve
    ret = self.urlOpener.retrieve_unicode(url, size=size)
  File "/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/imdb/parser/http/__init__.py", line 261, in retrieve_unicode
    raise IMDbDataAccessError(
imdb._exceptions.IMDbDataAccessError: {'errcode': None, 'errmsg': 'None', 'url': 'https://www.imdb.com/title/tt1065073/reference', 'proxy': '', 'exception type': 'IOError', 'original exception': TimeoutError('The read operation timed out')}

Error processing movie 'Boyhood': {'errcode': None, 'errmsg': 'None', 'url': 'https://www.imdb.com/title/tt1065073/reference', 'proxy': '', 'exception type': 'IOError', 'original exception': TimeoutError('The read operation timed out')}
Processing movie 230/400: Frontera
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Processing movies from the year 2015...
Found 400 movies from the year 2015.
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Processing movie 315/400: Bad Asses on the Bayou
Processing movie 316/400: Barely Lethal
Processing movie 317/400: Haikyuu!! The Movie: The End and the Beginning
Processing movie 318/400: Infini
Processing movie 319/400: Sisters
Processing movie 320/400: Do You Believe?
Processing movie 321/400: Vendetta
Processing movie 322/400: Careful What You Wish For
Processing movie 323/400: Monster High: Haunted
Processing movie 324/400: Frozen Fever
Processing movie 325/400: Huevos: Little Rooster's Egg-Cellent Adventure
Processing movie 326/400: Teen Beach 2
Processing movie 327/400: The Scorpion King 4: Quest for Power
Processing movie 328/400: Mississippi Grind
Processing movie 329/400: While We're Young
Processing movie 330/400: The Smell of Us
Processing movie 331/400: Hyena Road
Processing movie 332/400: Wild Horses
Processing movie 333/400: Coin Locker Girl
Processing movie 334/400: SPL 2: A Time for Consequences
Processing movie 335/400: Martyrs
Processing movie 336/400: A Man Called Ove
Processing movie 337/400: Insurgent
Processing movie 338/400: Love & Mercy
Processing movie 339/400: Furious 7
Processing movie 340/400: Little Boy
Processing movie 341/400: Ricki and the Flash
Processing movie 342/400: Extraction
Processing movie 343/400: 3-Headed Shark Attack
Processing movie 344/400: The Final Girls
Processing movie 345/400: The Hateful Eight
Processing movie 346/400: Black Mass
Processing movie 347/400: The Big Short
Processing movie 348/400: Cobain: Montage of Heck
Processing movie 349/400: Joy
Processing movie 350/400: The Girl in the Book
Processing movie 351/400: Bridge of Spies
Processing movie 352/400: Point Break
Processing movie 353/400: 10000 Years Later
Processing movie 354/400: Winter on Fire: Ukraine's Fight for Freedom
Processing movie 355/400: Fifty Shades of Grey
Processing movie 356/400: No Escape
Processing movie 357/400: The Atticus Institute
Processing movie 358/400: Jupiter Ascending
Processing movie 359/400: The Little Prince
Processing movie 360/400: Blackhat
Processing movie 361/400: Howl
Processing movie 362/400: Dead Rising: Watchtower
Processing movie 363/400: Criminal Activities
Processing movie 364/400: Summertime
Processing movie 365/400: Far from the Madding Crowd
Processing movie 366/400: Akron
Processing movie 367/400: Shaun the Sheep Movie
Processing movie 368/400: Alvin and the Chipmunks: The Road Chip
Processing movie 369/400: X-Rated: The Greatest Adult Movies of All Time
Processing movie 370/400: Chronicles of the Ghostly Tribe
Processing movie 371/400: Paper Towns
Processing movie 372/400: Bajirao Mastani
Processing movie 373/400: A Delicious Flight
Processing movie 374/400: Everest
Processing movie 375/400: Mr. Holmes
Processing movie 376/400: Tale of Tales
Processing movie 377/400: Palm Trees in the Snow
Processing movie 378/400: The Lady in the Van
Processing movie 379/400: Love the Coopers
Processing movie 380/400: Tangerine
Processing movie 381/400: The Longest Ride
Processing movie 382/400: Curve
Processing movie 383/400: Minions
Processing movie 384/400: How to Plan an Orgy in a Small Town
Processing movie 385/400: The Transporter Refueled
Processing movie 386/400: Open Season: Scared Silly
Processing movie 387/400: Kept Woman
Processing movie 388/400: Orange
Processing movie 389/400: The Empire of Corpses
Processing movie 390/400: The Wedding Ringer
Processing movie 391/400: Maze Runner: The Scorch Trials
Processing movie 392/400: The Revenant
Processing movie 393/400: Vice
Processing movie 394/400: Mune: Guardian of the Moon
Processing movie 395/400: Poltergeist
Processing movie 396/400: Extinction

Processing movie 397/400: The Benefactor
Processing movie 398/400: Seeds of Yesterday
Processing movie 399/400: Crown for Christmas
Processing movie 400/400: If There Be Thorns
Movie information has been saved to 'movie_all_info_2014_to_2015.csv'

1.2 Basic Movie Data Cleaning

1.2.1 Read Dataset

data = pd.read_csv('/Users/heshuqing0305/Downloads/merged_all_movies.csv')
data

	Title	Year	IMDb ID	Director	Rating	Votes	Box Office	Budget	Genres	Actors	Languages	Countries
0	X-Men: Days of Future Past	2014.0	1877832	Bryan Singer	7.9	765945.0	$748,121,534, 28 Feb 2015	$200,000,000 (estimated)	Action, Adventure, Sci-Fi, Thriller	Hugh Jackman, James McAvoy, Michael Fassbender...	English, Vietnamese, French	United Kingdom, United States, Canada
1	If I Stay	2014.0	1355630	R.J. Cutler	6.7	129700.0	$78,874,843	$11,000,000 (estimated)	Drama, Fantasy, Music, Romance, Sci-Fi	Chloë Grace Moretz, Mireille Enos, Jamie Black...	English	United States
2	Leviathan	1989.0	97737	George P. Cosmatos	5.8	25227.0	NaN	$25,000,000 (estimated)	Adventure, Horror, Mystery, Sci-Fi, Thriller	Peter Weller, Richard Crenna, Amanda Pays, Dan...	English	Italy, United States
3	The Purge: Anarchy	2014.0	2975578	James DeMonaco	6.4	167191.0	$110,602,999	$9,000,000 (estimated)	Action, Horror, Sci-Fi, Thriller	Frank Grillo, Carmen Ejogo, Zach Gilford, Kiel...	English	United States, France
4	Adult World	2013.0	1067765	Scott Coffey	6.1	18975.0	NaN	$4,000,000 (estimated)	Comedy, Drama	Emma Roberts, Summer Shelton, Chris Riggi, Sha...	English	United States
...	...	...	...	...	...	...	...	...	...	...	...	...
4383	The First Omen	2024.0	5672290	Arkasha Stevenson	6.5	62703.0	$50,760,266	$30,000,000 (estimated)	Horror	Nell Tiger Free, Ralph Ineson, Sonia Braga, Ta...	English, Italian	United States, Italy, Serbia, Canada
4384	Someone Like You	2024.0	22334258	Tyler Russell	5.8	1962.0	NaN	NaN	Romance	Sarah Fisher, Jake Allyn, Lynn Collins, Robyn ...	English	United States
4385	Monster Summer	2024.0	3954936	David Henrie	5.7	3359.0	NaN	NaN	Adventure, Fantasy, Mystery	Mel Gibson, Mason Thames, Julian Lerner, Abby ...	English	United States
4386	Juror #2	2024.0	27403986	Clint Eastwood	7.0	87604.0	NaN	$30,000,000 (estimated)	Crime, Drama, Mystery, Thriller	Nicholas Hoult, Zoey Deutch, Megan Mieduch, To...	English	United States
4387	Wolfs	2024.0	14257582	Jon Watts	6.5	76575.0	NaN	NaN	Crime, Thriller	Amy Ryan, Austin Abrams, George Clooney, Brad ...	English	United Kingdom, United States

4388 rows × 12 columns

filtered_data1 = data[(data['Year'] >= 2014) & (data['Year'] <= 2024)]

filtered_data1

	Title	Year	IMDb ID	Director	Rating	Votes	Box Office	Budget	Genres	Actors	Languages	Countries
0	X-Men: Days of Future Past	2014.0	1877832	Bryan Singer	7.9	765945.0	$748,121,534, 28 Feb 2015	$200,000,000 (estimated)	Action, Adventure, Sci-Fi, Thriller	Hugh Jackman, James McAvoy, Michael Fassbender...	English, Vietnamese, French	United Kingdom, United States, Canada
1	If I Stay	2014.0	1355630	R.J. Cutler	6.7	129700.0	$78,874,843	$11,000,000 (estimated)	Drama, Fantasy, Music, Romance, Sci-Fi	Chloë Grace Moretz, Mireille Enos, Jamie Black...	English	United States
3	The Purge: Anarchy	2014.0	2975578	James DeMonaco	6.4	167191.0	$110,602,999	$9,000,000 (estimated)	Action, Horror, Sci-Fi, Thriller	Frank Grillo, Carmen Ejogo, Zach Gilford, Kiel...	English	United States, France
5	Batman: Assault on Arkham	2014.0	3139086	Jay Oliva, Ethan Spaulding	7.4	38681.0	NaN	NaN	Animation, Action, Adventure, Crime, Fantasy, ...	Kevin Conroy, Neal McDonough, Hynden Walch, Ma...	English	United States
6	Get on Up	2014.0	2473602	Tate Taylor	6.9	26527.0	NaN	$30,000,000 (estimated)	Biography, Drama, Music	Chadwick Boseman, Nelsan Ellis, Dan Aykroyd, V...	English	United Kingdom, United States
...	...	...	...	...	...	...	...	...	...	...	...	...
4383	The First Omen	2024.0	5672290	Arkasha Stevenson	6.5	62703.0	$50,760,266	$30,000,000 (estimated)	Horror	Nell Tiger Free, Ralph Ineson, Sonia Braga, Ta...	English, Italian	United States, Italy, Serbia, Canada
4384	Someone Like You	2024.0	22334258	Tyler Russell	5.8	1962.0	NaN	NaN	Romance	Sarah Fisher, Jake Allyn, Lynn Collins, Robyn ...	English	United States
4385	Monster Summer	2024.0	3954936	David Henrie	5.7	3359.0	NaN	NaN	Adventure, Fantasy, Mystery	Mel Gibson, Mason Thames, Julian Lerner, Abby ...	English	United States
4386	Juror #2	2024.0	27403986	Clint Eastwood	7.0	87604.0	NaN	$30,000,000 (estimated)	Crime, Drama, Mystery, Thriller	Nicholas Hoult, Zoey Deutch, Megan Mieduch, To...	English	United States
4387	Wolfs	2024.0	14257582	Jon Watts	6.5	76575.0	NaN	NaN	Crime, Thriller	Amy Ryan, Austin Abrams, George Clooney, Brad ...	English	United Kingdom, United States

4070 rows × 12 columns

1.2.2 Data Transformation and Standardization

# Extract the amount part and process it
filtered_data1['Budget_Amount'] = filtered_data1['Budget'].str.extract(r'(\$[0-9,]+)')[0]

# Process the amount by removing $ and commas
filtered_data1['Budget_Amount'] = filtered_data1['Budget_Amount'].str.replace(r'[\$,]', '', regex=True)

# Convert the amount to numeric, handling conversion errors
filtered_data1['Budget_Amount'] = pd.to_numeric(filtered_data1['Budget_Amount'], errors='coerce')

# Extract the Box Office amount part and process it
filtered_data1['Box_Office_Amount'] = filtered_data1['Box Office'].str.extract(r'(\$[0-9,]+)')[0]

# Process the amount by removing $ and commas
filtered_data1['Box_Office_Amount'] = filtered_data1['Box_Office_Amount'].str.replace(r'[\$,]', '', regex=True)

# Convert the amount to numeric, handling conversion errors
filtered_data1['Box_Office_Amount'] = pd.to_numeric(filtered_data1['Box_Office_Amount'], errors='coerce')

# Store the processed data in filtered_data2
filtered_data2 = filtered_data1.copy()

/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_24474/1804602363.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  filtered_data1['Budget_Amount'] = filtered_data1['Budget'].str.extract(r'(\$[0-9,]+)')[0]
/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_24474/1804602363.py:5: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  filtered_data1['Budget_Amount'] = filtered_data1['Budget_Amount'].str.replace(r'[\$,]', '', regex=True)
/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_24474/1804602363.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  filtered_data1['Budget_Amount'] = pd.to_numeric(filtered_data1['Budget_Amount'], errors='coerce')
/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_24474/1804602363.py:11: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  filtered_data1['Box_Office_Amount'] = filtered_data1['Box Office'].str.extract(r'(\$[0-9,]+)')[0]
/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_24474/1804602363.py:14: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  filtered_data1['Box_Office_Amount'] = filtered_data1['Box_Office_Amount'].str.replace(r'[\$,]', '', regex=True)
/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_24474/1804602363.py:17: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  filtered_data1['Box_Office_Amount'] = pd.to_numeric(filtered_data1['Box_Office_Amount'], errors='coerce')

filtered_data2

	Title	Year	IMDb ID	Director	Rating	Votes	Box Office	Budget	Genres	Actors	Languages	Countries	Budget_Amount	Box_Office_Amount
0	X-Men: Days of Future Past	2014.0	1877832	Bryan Singer	7.9	765945.0	$748,121,534, 28 Feb 2015	$200,000,000 (estimated)	Action, Adventure, Sci-Fi, Thriller	Hugh Jackman, James McAvoy, Michael Fassbender...	English, Vietnamese, French	United Kingdom, United States, Canada	200000000.0	748121534.0
1	If I Stay	2014.0	1355630	R.J. Cutler	6.7	129700.0	$78,874,843	$11,000,000 (estimated)	Drama, Fantasy, Music, Romance, Sci-Fi	Chloë Grace Moretz, Mireille Enos, Jamie Black...	English	United States	11000000.0	78874843.0
3	The Purge: Anarchy	2014.0	2975578	James DeMonaco	6.4	167191.0	$110,602,999	$9,000,000 (estimated)	Action, Horror, Sci-Fi, Thriller	Frank Grillo, Carmen Ejogo, Zach Gilford, Kiel...	English	United States, France	9000000.0	110602999.0
5	Batman: Assault on Arkham	2014.0	3139086	Jay Oliva, Ethan Spaulding	7.4	38681.0	NaN	NaN	Animation, Action, Adventure, Crime, Fantasy, ...	Kevin Conroy, Neal McDonough, Hynden Walch, Ma...	English	United States	NaN	NaN
6	Get on Up	2014.0	2473602	Tate Taylor	6.9	26527.0	NaN	$30,000,000 (estimated)	Biography, Drama, Music	Chadwick Boseman, Nelsan Ellis, Dan Aykroyd, V...	English	United Kingdom, United States	30000000.0	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4383	The First Omen	2024.0	5672290	Arkasha Stevenson	6.5	62703.0	$50,760,266	$30,000,000 (estimated)	Horror	Nell Tiger Free, Ralph Ineson, Sonia Braga, Ta...	English, Italian	United States, Italy, Serbia, Canada	30000000.0	50760266.0
4384	Someone Like You	2024.0	22334258	Tyler Russell	5.8	1962.0	NaN	NaN	Romance	Sarah Fisher, Jake Allyn, Lynn Collins, Robyn ...	English	United States	NaN	NaN
4385	Monster Summer	2024.0	3954936	David Henrie	5.7	3359.0	NaN	NaN	Adventure, Fantasy, Mystery	Mel Gibson, Mason Thames, Julian Lerner, Abby ...	English	United States	NaN	NaN
4386	Juror #2	2024.0	27403986	Clint Eastwood	7.0	87604.0	NaN	$30,000,000 (estimated)	Crime, Drama, Mystery, Thriller	Nicholas Hoult, Zoey Deutch, Megan Mieduch, To...	English	United States	30000000.0	NaN
4387	Wolfs	2024.0	14257582	Jon Watts	6.5	76575.0	NaN	NaN	Crime, Thriller	Amy Ryan, Austin Abrams, George Clooney, Brad ...	English	United Kingdom, United States	NaN	NaN

4070 rows × 14 columns

print(filtered_data2.isnull().sum())

Title                   0
Year                    0
IMDb ID                 0
Director              194
Rating                 77
Votes                  77
Box Office           2966
Budget               2241
Genres                 37
Actors                 58
Languages              92
Countries              66
Budget_Amount        2491
Box_Office_Amount    2973
dtype: int64

1.2.3 Handle Missing Values

# Remove rows with NaN values in the Box Office and Budget columns
filtered_data2.dropna(subset=['Box_Office_Amount'], inplace=True)

print(filtered_data2.isnull().sum())

Title                  0
Year                   0
IMDb ID                0
Director               0
Rating                 0
Votes                  0
Box Office             0
Budget               177
Genres                 0
Actors                 0
Languages              2
Countries              0
Budget_Amount        222
Box_Office_Amount      0
dtype: int64

# Convert the Year column to integer type
filtered_data2['Year'] = filtered_data2['Year'].astype(int)

# Define a function to fill missing values
def fill_budget_with_median(group):
    median_budget = group['Budget_Amount'].median()
    group['Budget_Amount'].fillna(median_budget, inplace=True)
    return group

# Group by Year and apply the filling function
filtered_data2 = filtered_data2.groupby('Year').apply(fill_budget_with_median)

# Output the number of missing values after filling
print(filtered_data2['Budget_Amount'].isnull().sum())  # Confirm if there are any missing values

0

/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_24474/2470676192.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  group['Budget_Amount'].fillna(median_budget, inplace=True)
/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_24474/2470676192.py:8: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  filtered_data2 = filtered_data2.groupby('Year').apply(fill_budget_with_median)

# Drop specified columns and rows with NaN values

filtered_data3 = filtered_data2.drop(columns=['Box Office', 'Budget', 'IMDb ID'])
filtered_data3 = filtered_data3.dropna(subset=['Languages'])
filtered_data3

		Title	Year	Director	Rating	Votes	Genres	Actors	Languages	Countries	Budget_Amount	Box_Office_Amount
Year
2014	0	X-Men: Days of Future Past	2014	Bryan Singer	7.9	765945.0	Action, Adventure, Sci-Fi, Thriller	Hugh Jackman, James McAvoy, Michael Fassbender...	English, Vietnamese, French	United Kingdom, United States, Canada	200000000.0	748121534.0
	1	If I Stay	2014	R.J. Cutler	6.7	129700.0	Drama, Fantasy, Music, Romance, Sci-Fi	Chloë Grace Moretz, Mireille Enos, Jamie Black...	English	United States	11000000.0	78874843.0
	3	The Purge: Anarchy	2014	James DeMonaco	6.4	167191.0	Action, Horror, Sci-Fi, Thriller	Frank Grillo, Carmen Ejogo, Zach Gilford, Kiel...	English	United States, France	9000000.0	110602999.0
	7	Doraemon: New Nobita's Great Demon-Peko and th...	2014	Shinnosuke Yakuwa	6.9	1408.0	Animation, Action, Adventure, Comedy, Family, ...	Wasabi Mizuta, Megumi Ôhara, Miku Natsume, Tom...	Japanese	Japan	33989696.0	35861856.0
	8	Godzilla	2014	Gareth Edwards	6.4	452077.0	Action, Adventure, Sci-Fi, Thriller	Aaron Taylor-Johnson, CJ Adams, Ken Watanabe, ...	English, Japanese	United States, Japan	160000000.0	529076069.0
...	...	...	...	...	...	...	...	...	...	...	...	...
2024	4280	Dune: Part Two	2024	Denis Villeneuve	8.5	604361.0	Action, Adventure, Drama, Sci-Fi	Timothée Chalamet, Zendaya, Rebecca Ferguson, ...	English	United States, Canada, United Arab Emirates, H...	190000000.0	714644358.0
	4298	Winnie-the-Pooh: Blood and Honey 2	2024	Rhys Frake-Waterfield	4.7	11004.0	Horror, Thriller	Scott Chambers, Tallulah Evans, Ryan Oliva, Le...	English	United Kingdom, United States	1000000.0	7049397.0
	4313	Despicable Me 4	2024	Chris Renaud, Patrick Delage	6.2	60323.0	Animation, Adventure, Comedy, Crime, Family, S...	Steve Carell, Pierre Coffin, Will Ferrell, Sof...	English, French	United States	100000000.0	969126452.0
	4329	Venom: The Last Dance	2024	Kelly Marcel	6.0	100031.0	Action, Adventure, Sci-Fi, Thriller	Tom Hardy, Chiwetel Ejiofor, Juno Temple, Rhys...	English	United States, United Kingdom, Canada	120000000.0	478835892.0
	4383	The First Omen	2024	Arkasha Stevenson	6.5	62703.0	Horror	Nell Tiger Free, Ralph Ineson, Sonia Braga, Ta...	English, Italian	United States, Italy, Serbia, Canada	30000000.0	50760266.0

1095 rows × 11 columns

# Convert Votes, Budget_Amount, and Box_Office_Amount to integer type
filtered_data3['Votes'] = filtered_data3['Votes'].astype(int)
filtered_data3['Budget_Amount'] = filtered_data3['Budget_Amount'].astype(int)
filtered_data3['Box_Office_Amount'] = filtered_data3['Box_Office_Amount'].astype(int)

# Assign the processed dataset to new_data
new_data = filtered_data3.copy()

# Output the information of new_data
print(new_data.info())

<class 'pandas.core.frame.DataFrame'>
MultiIndex: 1095 entries, (2014, 0) to (2024, 4383)
Data columns (total 11 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Title              1095 non-null   object 
 1   Year               1095 non-null   int64  
 2   Director           1095 non-null   object 
 3   Rating             1095 non-null   float64
 4   Votes              1095 non-null   int64  
 5   Genres             1095 non-null   object 
 6   Actors             1095 non-null   object 
 7   Languages          1095 non-null   object 
 8   Countries          1095 non-null   object 
 9   Budget_Amount      1095 non-null   int64  
 10  Box_Office_Amount  1095 non-null   int64  
dtypes: float64(1), int64(4), object(6)
memory usage: 138.6+ KB
None

# Export new_data to a CSV file
new_data.to_csv('new_data.csv', index=False)

print("Dataset has been successfully exported as new_data.csv")

1.3 Basic Movie Data Analysis

1.3.1 Box Office Analysis (Data Standardization)

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import StandardScaler

df_Box_Office = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv')

# Check the budget and box office for 0 or negative values (because log(0) is undefined)
print("Box office minimum:", df_Box_Office['Box_Office_Amount'].min())

Box office minimum: 11

# Logarithm conversion (add 1 to prevent log(0))
df_Box_Office['Log_Box_Office'] = np.log1p(df_Box_Office['Box_Office_Amount'])

# Verify the conversion result
print("the conversion result：")
print(df_Box_Office[['Box_Office_Amount', 'Log_Box_Office']].head())

the conversion result：
   Box_Office_Amount  Log_Box_Office
0          748121534       20.433076
1           78874843       18.183373
2          110602999       18.521458
3           35861856       17.395185
4          529076069       20.086643

# Standardization (Z-score standardization)
scaler = StandardScaler()
df_Box_Office[['Std_Log_Box_Office']] = scaler.fit_transform(
    df_Box_Office[[ 'Log_Box_Office']])

# result
print("\nExample of post-processing data：")
print(df_Box_Office[[ 'Box_Office_Amount', 'Log_Box_Office', 'Std_Log_Box_Office']].head())

Example of post-processing data：
   Box_Office_Amount  Log_Box_Office  Std_Log_Box_Office
0          748121534       20.433076            1.229599
1           78874843       18.183373            0.072563
2          110602999       18.521458            0.246442
3           35861856       17.395185           -0.332807
4          529076069       20.086643            1.051426

print(df_Box_Office.isnull().sum())
print((df_Box_Office['Box_Office_Amount'] < 0).sum())

Title                 0
Year                  0
Director              0
Rating                0
Votes                 0
Genres                0
Actors                0
Languages             0
Countries             0
Budget_Amount         0
Box_Office_Amount     0
Log_Box_Office        0
Std_Log_Box_Office    0
dtype: int64
0

print(df_Box_Office['Std_Log_Box_Office'].mean())
print(df_Box_Office['Std_Log_Box_Office'].std())

2.5955899023200005e-16
1.0004569339968865

import seaborn as sns

# Original distribution
plt.figure(figsize=(12,4))
plt.subplot(131)
sns.histplot(df['Box_Office_Amount'], kde=True)
plt.title('Original Box_Office')

# Logarithmic distribution
plt.subplot(132)
sns.histplot(df['Log_Box_Office'], kde=True)
plt.title('Log_Box_Office')

# Normalized distribution
plt.subplot(133)
sns.histplot(df['Std_Log_Box_Office'], kde=True)
plt.title('Standardized_Log_Box_Office')
plt.tight_layout()

/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):

1.3.1 Box Office Analysis (Data Analysis)

# 按年份分组并聚合标准化票房数据
yearly_stats = df_Box_Office.groupby('Year').agg({
    'Std_Log_Box_Office': ['mean', 'std'],  # 计算标准化票房的均值和标准差
    'Box_Office_Amount': ['sum', 'mean', 'median', 'count']  # 原始票房的统计数据
})

# 重命名列以提高可读性
yearly_stats.columns = ['Z_Score_Mean', 'Z_Score_Std', 
                       'Total_Box_Office', 'Mean_Box_Office', 
                       'Median_Box_Office', 'Movie_Count']

# 打印标准化票房数据的预览
print("=== Standardized Box Office Data Preview (Z-Scores) ===")
print(yearly_stats[['Z_Score_Mean', 'Z_Score_Std']])  # 专门显示Z-score相关数据

=== Standardized Box Office Data Preview (Z-Scores) ===
      Z_Score_Mean  Z_Score_Std
Year                           
2014      0.074158     0.856840
2015     -0.114843     1.080018
2016     -0.187971     1.013932
2017     -0.135768     1.124504
2018     -0.001760     1.034283
2019      0.082873     0.927611
2020     -0.432608     0.951242
2021     -0.183314     1.010060
2022      0.240855     0.770815
2023      0.749062     0.418046
2024      0.885033     0.532623

# 可视化标准化票房的年度趋势（保持原始可视化不变）
plt.figure(figsize=(12, 6))
sns.lineplot(data=yearly_stats, x=yearly_stats.index, y='Z_Score_Mean', 
             marker='o', color='darkblue', label='Standardized Box Office (Z-score)')
plt.axhline(y=0, color='red', linestyle='--', label='Mean Baseline (Z=0)')
plt.title('Annual Trend of Standardized Box Office (Z-scores, 2014-2024)')
plt.xlabel('Year')
plt.ylabel('Standardized Box Office (Z-score)')
plt.legend()
plt.grid(False)
plt.show()

/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):

# 打印数据信息
print("\n=== Total Box Office Data ===")
print(yearly_stats[['Total_Box_Office']])
print(f"\nHighest Total Box Office: {max_value:,.0f} in {max_year}")
print(f"Lowest Total Box Office: {min_value:,.0f} in {min_year}")

=== Total Box Office Data ===
      Total_Box_Office
Year                  
2014       26735258091
2015       24250509300
2016       25095621906
2017       30267235425
2018       26847946280
2019       25856728549
2020        4362973131
2021       11535644957
2022       16085133859
2023       20325215368
2024       16224732556

Highest Total Box Office: 30,267,235,425 in 2017
Lowest Total Box Office: 4,362,973,131 in 2020

# 可视化总票房的年度趋势
plt.figure(figsize=(12, 6))
sns.lineplot(data=yearly_stats, x=yearly_stats.index, y='Total_Box_Office', 
             marker='s', color='green')

# 计算最高点和最低点
max_value = yearly_stats['Total_Box_Office'].max()
min_value = yearly_stats['Total_Box_Office'].min()
max_year = yearly_stats['Total_Box_Office'].idxmax()
min_year = yearly_stats['Total_Box_Office'].idxmin()

# 标注最高点和最低点
plt.scatter(max_year, max_value, color='red', s=100, label=f'Peak: {max_value:,.0f} ({max_year})')
plt.scatter(min_year, min_value, color='blue', s=100, label=f'Lowest: {min_value:,.0f} ({min_year})')

# 设置标题和标签
plt.title('Annual Trend of Total Box Office (Original Value, 2014-2024)')
plt.xlabel('Year')
plt.ylabel('Total Box Office (USD)')
plt.grid(False)

# 显示图例
plt.legend()
plt.show()

/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):

# Create a DataFrame with the key point
key_point = {
    'Year': [2017, 2020, 2023],
    'Total Box Office (USD)': [30267235425, 4362973131, 20325215368],
    'Change Rate (YoY)': ['+20.6% (vs 2016)', '-83.1% (vs 2019)', '+26.3% (vs 2022)']
}

df_key_point = pd.DataFrame(key_point)

# Display the table
df_key_point

	Year	Total Box Office (USD)	Change Rate (YoY)
0	2017	30267235425	+20.6% (vs 2016)
1	2020	4362973131	-83.1% (vs 2019)
2	2023	20325215368	+26.3% (vs 2022)

1.3.2 Rating Analysis

df_Rating = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv')

# Calculate rating statistics by year
yearly_rating = df_Rating.groupby('Year')['Rating'].agg(['mean', 'median', 'std', 'count'])
print("Annual Rating Statistics:\n", yearly_rating)

Annual Rating Statistics:
           mean  median       std  count
Year                                   
2014  6.595804     6.6  0.924081    143
2015  6.536364     6.6  0.946530    132
2016  6.572189     6.5  0.875049    169
2017  6.546939     6.6  0.929450    147
2018  6.489781     6.6  0.983039    137
2019  6.490000     6.5  1.001645    110
2020  6.256522     6.4  1.001477     46
2021  6.250725     6.2  0.873065     69
2022  6.736667     6.8  0.800417     60
2023  6.646154     6.7  0.823779     52
2024  6.846667     6.7  0.790780     30

# Plot the trend chart
plt.figure(figsize=(12, 6))
sns.lineplot(data=yearly_rating, x=yearly_rating.index, y='mean', marker='o', label='Average Rating')

# Fill the area between the mean ± standard deviation
plt.fill_between(yearly_rating.index, 
                 yearly_rating['mean'] - yearly_rating['std'], 
                 yearly_rating['mean'] + yearly_rating['std'],
                 alpha=0.2, label='Standard Deviation Range')

# Set the title and labels for the plot
plt.title('Annual Trend of Average Movie Ratings', fontsize=15)
plt.xlabel('Year')
plt.ylabel('Rating')
plt.grid(False)
plt.legend()
plt.show()

/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):
/Users/heshuqing0305/anaconda3/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):

# Perform significance test on the trend
slope, intercept, r_value, p_value, std_err = stats.linregress(
    yearly_rating.index, yearly_rating['mean'])
print(f"\nTrend Significance Test: Slope = {slope:.3f} (p-value = {p_value:.3f})")

Trend Significance Test: Slope = 0.012 (p-value = 0.495)

1.3.3 Genres Analysis

import pandas as pd
import matplotlib.pyplot as plt

df_Genres = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv')

df_Genres['Genres'] = df_Genres['Genres'].str.split(',').apply(
    lambda x: [g.strip() for g in x if g.strip()]
)

# Add a period column (every 3 years)
df_Genres['Period'] = pd.cut(
    df_Genres['Year'],
    bins=[2013, 2016, 2019, 2022, 2025],
    labels=['2014-2016', '2017-2019', '2020-2022', '2023-2024']
)

# View genre count distribution
genre_counts = df_Genres['Genres'].explode().value_counts()
print("Top 10 most frequent genres:")
print(genre_counts.head(10))

# Check period distribution
print("\nNumber of movies in each period:")
print(df_Genres['Period'].value_counts())

Top 10 most frequent genres:
Genres
Drama        525
Action       471
Adventure    418
Thriller     409
Comedy       383
Fantasy      276
Sci-Fi       258
Crime        198
Family       195
Mystery      190
Name: count, dtype: int64

Number of movies in each period:
Period
2014-2016    444
2017-2019    394
2020-2022    175
2023-2024     82
Name: count, dtype: int64

# Count genre frequency by period
genre_trend = (
    df_Genres.explode('Genres')
    .groupby(['Period', 'Genres'])
    .size()
    .unstack()
    .fillna(0)
)

# Calculate proportions
genre_percent = genre_trend.div(genre_trend.sum(axis=1), axis=0) * 100

# Filter TOP 10 genres
top_genres = genre_percent.sum().nlargest(10).index

# Print data table
print("Proportion data of genres by period (%):")
print(genre_percent[top_genres].round(1))  # Keep 1 decimal place

Proportion data of genres by period (%):
Genres     Action  Adventure  Drama  Thriller  Comedy  Fantasy  Sci-Fi  \
Period                                                                   
2014-2016    11.4        9.2   14.9      10.1     9.3      5.5     5.0   
2017-2019    10.3        9.5   13.4       9.9     9.6      6.8     6.6   
2020-2022    14.0       10.8    9.4      11.1     8.5      7.6     7.1   
2023-2024    10.6       15.2    6.3       6.3     9.2      9.7     8.3   

Genres     Family  Crime  Animation  
Period                               
2014-2016     4.2    5.4        3.2  
2017-2019     4.8    4.4        3.4  
2020-2022     4.6    4.6        5.2  
2023-2024     7.2    4.0        6.6  

/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_77558/2248613393.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  .groupby(['Period', 'Genres'])

# Data preparation
genre_trend = (
    df_Genres.explode('Genres')
    .groupby(['Period', 'Genres'])
    .size()
    .unstack()
    .fillna(0)
)
genre_percent = genre_trend.div(genre_trend.sum(axis=1), axis=0) * 100
top_genres = genre_percent.sum().nlargest(10).index

# Set color cycle
colors = plt.cm.tab10.colors

# Create 10 subplots (2 rows, 5 columns)
fig, axes = plt.subplots(2, 5, figsize=(25, 12))
plt.subplots_adjust(hspace=0.4, wspace=0.3)

# Print data table
print("TOP 10 genre proportion data table (%):")
display(genre_percent[top_genres].style
        .background_gradient(cmap='Blues', axis=0)
        .format("{:.1f}%"))

# Plot each subplot
for i, genre in enumerate(top_genres):
    ax = axes[i//5, i%5]
    ax.plot(genre_percent.index, genre_percent[genre], 
            color=colors[i], marker='o', linewidth=3, markersize=8)
    
    # Add data labels
    for x, y in zip(genre_percent.index, genre_percent[genre]):
        ax.text(x, y, f'{y:.1f}%', ha='center', va='bottom', fontsize=10)
    
    ax.set_title(f'{genre} Proportion Trend', fontsize=14, pad=15)
    ax.set_ylim(0, genre_percent[top_genres].max().max()*1.1)
    ax.grid(True, linestyle=':', alpha=0.6)
    ax.set_xlabel('Period', fontsize=12)
    ax.set_ylabel('Proportion (%)', fontsize=12)
    ax.tick_params(axis='x', rotation=45)

# Add main title
plt.suptitle('Trend of TOP 10 Movie Genre Proportions (2014-2024)', 
             y=1.02, fontsize=18, weight='bold')

plt.show()

## Note: TOP10 is selected based on the sum of the proportion of each time period
# Reflects the type with the highest overall market share and the largest non-absolute number

TOP 10 genre proportion data table (%):

/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_77558/1113400692.py:4: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  .groupby(['Period', 'Genres'])

Genres	Action	Adventure	Drama	Thriller	Comedy	Fantasy	Sci-Fi	Family	Crime	Animation
Period
2014-2016	11.4%	9.2%	14.9%	10.1%	9.3%	5.5%	5.0%	4.2%	5.4%	3.2%
2017-2019	10.3%	9.5%	13.4%	9.9%	9.6%	6.8%	6.6%	4.8%	4.4%	3.4%
2020-2022	14.0%	10.8%	9.4%	11.1%	8.5%	7.6%	7.1%	4.6%	4.6%	5.2%
2023-2024	10.6%	15.2%	6.3%	6.3%	9.2%	9.7%	8.3%	7.2%	4.0%	6.6%

# Split and clean the 'Genres' column
df_Genres['Genres'] = df_Genres['Genres'].str.split(',').apply(
    lambda x: [g.strip() for g in x if g.strip()]
)

# Print descriptive statistics for 'Rating' and 'Votes'
print(df_Genres[['Rating', 'Votes']].describe())

            Rating         Votes
count  1095.000000  1.095000e+03
mean      6.535525  1.756549e+05
std       0.926014  2.204085e+05
min       2.800000  2.100000e+02
25%       6.000000  4.039750e+04
50%       6.600000  1.024250e+05
75%       7.200000  2.123765e+05
max       8.700000  2.296980e+06

# 2. Calculate key metrics
df_Genres['ROI'] = (df_Genres['Box_Office_Amount'] - df_Genres['Budget_Amount']) / df_Genres['Budget_Amount']
df_Genres['Revenue_per_Million'] = df_Genres['Box_Office_Amount'] / (df_Genres['Budget_Amount'] / 1e6)

def weighted_rating(df, m=2500, C=6.5):
    """IMDb weighted rating formula"""
    v = df['Votes']  # Number of votes
    R = df['Rating']  # Rating
    return (v / (v + m) * R) + (m / (v + m) * C)  # Weighted score calculation

# Apply weighted rating
df_Genres['Weighted_Rating'] = df_Genres.apply(weighted_rating, axis=1)

# Aggregate statistics by genre
genre_stats = (
    df_Genres.explode('Genres')
    .groupby('Genres')
    .agg({
        'Rating': 'mean',
        'Weighted_Rating': 'mean',
        'Votes': 'sum',
        'Box_Office_Amount': 'median',
        'Budget_Amount': 'median',
        'Title': 'count'
    })
    .rename(columns={'Title': 'Movie_count'})
    .round(2)
)

# Filter out genres with insufficient movie counts
genre_stats = genre_stats[genre_stats['Movie_count'] >= 10]

# Define quantile thresholds (top 25%)
Q1_boxoffice = genre_stats['Box_Office_Amount'].quantile(0.75)
Q1_rating = genre_stats['Weighted_Rating'].quantile(0.75)

# Normalize to a 0-1 range (for visualization)
scaler = MinMaxScaler()
genre_stats[['Norm_Rating', 'Norm_BoxOffice']] = scaler.fit_transform(
    genre_stats[['Weighted_Rating', 'Box_Office_Amount']]
)

# Unified classification logic
conditions = [
    (genre_stats['Weighted_Rating'] > Q1_rating) & (genre_stats['Box_Office_Amount'] > Q1_boxoffice),
    (genre_stats['Weighted_Rating'] > Q1_rating),
    (genre_stats['Box_Office_Amount'] > Q1_boxoffice)
]
choices = ['Both popular and profitable', 'Popular but not profitable', 'Profitable but not popular']
genre_stats['Preference_Type'] = np.select(conditions, choices, default='Other')

print("Statistics after unified classification:")
display_cols = ['Rating', 'Weighted_Rating', 'Box_Office_Amount', 'Movie_count', 'Preference_Type']
print(genre_stats[display_cols].sort_values('Weighted_Rating', ascending=False))

Statistics after unified classification:
           Rating  Weighted_Rating  Box_Office_Amount  Movie_count             Preference_Type
Genres                                                                                        
Biography    7.15             7.14         50374432.0           97  Popular but not profitable
Music        7.11             7.09         86165646.0           35  Popular but not profitable
History      7.06             7.05         49287456.0           78  Popular but not profitable
Sport        6.99             6.97         73581138.5           26  Popular but not profitable
War          6.87             6.90         90167512.5           48  Popular but not profitable
Drama        6.79             6.80         63609589.0          525                       Other
Western      6.78             6.79         43857733.5           18                       Other
Animation    6.69             6.72        163732928.0          161  Profitable but not popular
Romance      6.59             6.60         68674633.0          154                       Other
Crime        6.55             6.56         76707000.0          198                       Other
Comedy       6.52             6.55        112343513.0          383                       Other
Family       6.51             6.55        197183546.0          195  Profitable but not popular
Adventure    6.52             6.55        216841230.5          418  Profitable but not popular
Fantasy      6.46             6.50        152722676.0          276                       Other
Musical      6.48             6.50        251313000.0           44  Profitable but not popular
Sci-Fi       6.46             6.49        208255719.5          258  Profitable but not popular
Action       6.45             6.48        145374099.0          471                       Other
Thriller     6.36             6.39         69633110.0          409                       Other
Mystery      6.27             6.29         51763753.5          190                       Other
Horror       5.94             6.02         54104225.0          147                       Other

# Create scatter plot
plt.figure(figsize=(12, 8))
scatter = sns.scatterplot(
    data=genre_stats,
    x='Norm_BoxOffice',
    y='Norm_Rating',
    hue='Preference_Type',
    palette={
        'Both popular and profitable': 'green',
        'Popular but not profitable': 'blue',
        'Profitable but not popular': 'red',
        'Other': 'gray'
    },
    s=100,
    alpha=0.8
)

# Add genre labels
for line in range(len(genre_stats)):
    plt.text(
        genre_stats['Norm_BoxOffice'].iloc[line] + 0.02,
        genre_stats['Norm_Rating'].iloc[line],
        genre_stats.index[line],
        fontsize=9
    )

plt.axhline(y=0.7, color='black', linestyle='--', alpha=0.3)  # Rating threshold line
plt.axvline(x=0.7, color='black', linestyle='--', alpha=0.3)  # Box office threshold line
plt.title("Movie Genre Popularity vs. Box Office (Based on Quantile Threshold Classification)", fontsize=14)
plt.xlabel("Normalized Box Office (Quantile Adjusted)")
plt.ylabel("Normalized Weighted Rating")
plt.legend(bbox_to_anchor=(1.05, 1))
plt.tight_layout()
plt.show()

import pandas as pd
import matplotlib.pyplot as plt

# 1. Enhanced Superhero Movie Detection
superhero_keywords = [
    'Marvel', 'Avengers', 'Iron Man', 'Captain America', 'Thor', 'Hulk',
    'Black Widow', 'Black Panther', 'Spider-Man', 'Doctor Strange',
    'Guardians of the Galaxy', 'Ant-Man', 'Deadpool', 'Wolverine', 'X-Men',
    'Fantastic Four', 'Shang-Chi', 'Eternals', 'Captain Marvel', 'Hawkeye',
    'DC', 'Batman', 'Superman', 'Wonder Woman', 'Aquaman', 'Flash',
    'Green Lantern', 'Justice League', 'Suicide Squad', 'Shazam',
    'Blue Beetle', 'Black Adam', 'Joker', 'Harley Quinn', 'Cyborg',
    'Ironman', 'Cap America', 'Spidey', 'Xmen', 'X-men'
]

pattern = '|'.join(superhero_keywords)

# 2. Data Preparation
superhero = df_Genres[
    df_Genres['Title'].str.contains(pattern, case=False, na=False) |
    df_Genres['Actors'].str.contains(pattern, case=False, na=False) |
    df_Genres['Director'].str.contains(pattern, case=False, na=False)
]

# 3. Filter years with sufficient data (minimum 3 movies)
yearly_counts = superhero['Year'].value_counts()
valid_years = yearly_counts[yearly_counts >= 3].index
superhero = superhero[superhero['Year'].isin(valid_years)]

# 4. Trend Analysis
trend = superhero.groupby('Year').agg({
    'Box_Office_Amount': 'median',
    'Rating': 'mean'
}).loc[2014:2024]

# 5. Additional Analysis
# Calculate the annual growth rate of box office and ratings
trend['Box_Office_Growth'] = trend['Box_Office_Amount'].pct_change() * 100
trend['Rating_Change'] = trend['Rating'].diff()

# 6. Display Results
print("=== Box Office Data (Median) ===")
print(trend['Box_Office_Amount'].to_string())

print("\n=== Rating Data (Average) ===")
print(trend['Rating'].to_string())

# 7. Market Analysis
average_growth_rate = trend['Box_Office_Growth'].mean()
last_growth_rate = trend['Box_Office_Growth'].iloc[-3:].mean()

print("\n=== Market Analysis ===")
print(f"Total movies analyzed: {superhero.shape[0]}")
print(f"Last 3 years average growth rate: {last_growth_rate:.1f}%")
print(f"Overall average growth rate: {average_growth_rate:.1f}%")

# 8. Conclusion
if last_growth_rate < 5 or trend['Rating'].iloc[-1] < trend['Rating'].mean():
    print("\nConclusion: Potential market saturation risk (growth rate < 5% or declining ratings)")
else:
    print("\nConclusion: No significant saturation risk detected")

# 9. Visualization
plt.figure(figsize=(14, 8))

# Box Office Plot
plt.subplot(2, 1, 1)
plt.plot(trend.index, trend['Box_Office_Amount'], marker='o', color='steelblue', label='Median Box Office')
plt.title('Box Office Median Over Years')
plt.xlabel('Year')
plt.ylabel('Box Office (USD)')
plt.grid(False)
plt.legend()

# Rating Plot
plt.subplot(2, 1, 2)
plt.plot(trend.index, trend['Rating'], marker='o', color='green', label='Average Rating')
plt.title('Average Rating Over Years')
plt.xlabel('Year')
plt.ylabel('Average Rating (0-10)')
plt.grid(False)
plt.legend()

plt.tight_layout()
plt.show()

=== Box Office Data (Median) ===
Year
2014    711874447.5
2015     52347709.0
2016    439418062.0
2017     46372519.0
2018    580293875.5
2019    720111483.5
2020    166370364.0
2021    405932180.5
2022    760928081.0
2023    371574860.0

=== Rating Data (Average) ===
Year
2014    7.287500
2015    6.487500
2016    6.800000
2017    6.646154
2018    7.400000
2019    6.875000
2020    6.100000
2021    7.325000
2022    6.685714
2023    6.375000

=== Market Analysis ===
Total movies analyzed: 77
Last 3 years average growth rate: 60.1%
Overall average growth rate: 204.0%

Conclusion: Potential market saturation risk (growth rate < 5% or declining ratings)

2.0 Changes in audience attention to movie themes (Audience Comment Data)

2.1 Fetch Movie Comments from Reddit Based on Movie Data

import praw
import pandas as pd
from datetime import datetime
import re

# Initialize Reddit client
reddit_read_only = praw.Reddit(client_id="xElg5p_zl9vg2WSOxBt7XQ",
                                 client_secret="HsbH8f9aPHO9ro7Prlsv1ohL4dp91w",
                                 user_agent="app scraping")

# Define the time range
start_date = datetime(2014, 1, 1)
end_date = datetime(2024, 12, 31)

# Read movie dataset
movie_data = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv')  # Ensure the file path is correct

# Create a list to store matching comments
comments_list = []

# Iterate through each movie in the dataset
for index, row in movie_data.iterrows():
    movie_title = row['Title']
    movie_year = row['Year']
    print(f"Fetching comments for: {movie_title} ({movie_year})")

    # Search for posts related to the movie
    subreddit = reddit_read_only.subreddit("movies")
    search_results = subreddit.search(movie_title, sort="relevance", limit=300)

    # Create a list to store filtered posts
    filtered_posts = []
    for post in search_results:
        post_time = datetime.utcfromtimestamp(post.created_utc)

        # Check if the post's time is within the range
        if start_date <= post_time <= end_date and movie_title.lower() in post.title.lower():
            filtered_posts.append(post)

        # Limit to a maximum of 50 posts
        if len(filtered_posts) >= 50:
            break

    # Print the number of posts found
    print(f"Total posts found for {movie_title}: {len(filtered_posts)}")

    # Variable to track the total number of comments for each movie
    total_comments_for_movie = 0

    # Iterate through the filtered posts
    for post in filtered_posts:
        print(f"Post Title: {post.title}")  # Print the post title

        post_author = str(post.author)
        post_time = datetime.utcfromtimestamp(post.created_utc).strftime('%Y-%m-%d %H:%M:%S')
        post_score = post.score
        post_comments_count = len(post.comments)  # Get the number of comments for the current post

        print(f"Comments Count: {post_comments_count}")  # Print the number of comments

        # Set the number of comments to extract based on interaction level
        if post_comments_count >= 100:
            comments_per_post_limit = 40  # High interaction post
        elif post_comments_count >= 50:
            comments_per_post_limit = 20  # Medium interaction post
        else:
            comments_per_post_limit = 10   # Low interaction post

        post.comments.replace_more(limit=0)  # Replace more comments

        # Extract comments
        comment_count = 0  # Comment counter
        for comment in post.comments.list():
            comment_time = datetime.utcfromtimestamp(comment.created_utc)

            # Check if the comment's time is within the range
            if start_date <= comment_time <= end_date:
                comments_list.append({
                    "Movie Title": movie_title,
                    "Year": movie_year,  # Add year
                    "Post Title": post.title,
                    "Post Author": post_author,
                    "Post Time": post_time,
                    "Post Score": post_score,
                    "Comment": comment.body,
                    "Comment Author": str(comment.author),
                    "Comment Time": comment_time.strftime('%Y-%m-%d %H:%M:%S'),
                    "Comment Score": comment.score
                })
                comment_count += 1
                total_comments_for_movie += 1  # Update the total comments for the movie

                # Check if the limit for comments from this post has been reached
                if comment_count >= comments_per_post_limit or total_comments_for_movie >= 1000:
                    break

        # If the limit for comments for the current movie has been reached, stop processing further posts
        if total_comments_for_movie >= 1000:
            print(f"Reached the limit of 1000 comments for {movie_title}.")
            break

# Create a DataFrame to save comments
comments_df = pd.DataFrame(comments_list)

# Display the comments DataFrame
print(comments_df)

# Save data to CSV file
comments_df.to_csv('all_movie_comments_combined_2014_2024.csv', index=False)
print("Comments data has been saved to 'all_movie_comments_combined_2014_2024.csv'.")

2.2 Comments Data Cleaning

2.2.1 Read Dataset

data = pd.read_csv('/Users/heshuqing0305/Desktop/7055_movie_data/all_movie_comments_combined_2014_2024.csv')
data

/var/folders/hs/x69nlpzd5yjdd1h1cfgct3lr0000gn/T/ipykernel_79156/2036488257.py:1: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.
  data = pd.read_csv('/Users/heshuqing0305/Desktop/7055_movie_data/all_movie_comments_combined_2014_2024.csv')

	Movie Title	Year	Post Title	Post Author	Post Time	Post Score	Comment	Comment Author	Comment Time	Comment Score
0	X-Men: Days of Future Past	2014	X-Men: Days of Future Past Trailer 2	Loopernator	2014-03-24 12:56:19	3165.0	I was excited to see some colossus love	PillsberryDboy4	2014-03-24 13:47:42	600.0
1	X-Men: Days of Future Past	2014	X-Men: Days of Future Past Trailer 2	Loopernator	2014-03-24 12:56:19	3165.0	"You'll need to go into the past, to fix every...	notshawnvaughn	2014-03-24 15:54:08	506.0
2	X-Men: Days of Future Past	2014	X-Men: Days of Future Past Trailer 2	Loopernator	2014-03-24 12:56:19	3165.0	Anna Paquin is still being listed as a cast me...	annachie-gordon	2014-03-24 13:58:09	114.0
3	X-Men: Days of Future Past	2014	X-Men: Days of Future Past Trailer 2	Loopernator	2014-03-24 12:56:19	3165.0	Was that Storm getting killed by a sentinel? I...	Moussekateer	2014-03-24 13:13:31	1095.0
4	X-Men: Days of Future Past	2014	X-Men: Days of Future Past Trailer 2	Loopernator	2014-03-24 12:56:19	3165.0	Superhero movies seem to really love football ...	NaN	2014-03-24 13:15:35	300.0
...	...	...	...	...	...	...	...	...	...	...
357554	The First Omen	2024	‘The Omen’ Prequel ‘The First Omen’ In the Wor...	MarvelsGrantMan136	2022-05-10 22:05:44	29.0	No I'm sorry you're too late, this isn't 2014 ...	Mission-Two1325	2022-05-11 06:29:25	2.0
357555	The First Omen	2024	‘The Omen’ Prequel ‘The First Omen’ In the Wor...	MarvelsGrantMan136	2022-05-10 22:05:44	29.0	Disney owns Fox now, so it will be “Omen The M...	whitehavenmansions	2022-05-11 15:06:06	1.0
357556	The First Omen	2024	What would a future sequel of the first omen l...	trover2345325	2024-04-14 15:37:06	0.0	I don't know , but I hope Russel Crowe is goin...	ferrarinobrakes	2024-04-14 19:22:47	2.0
357557	The First Omen	2024	The First Omen | Official Trailer | 20th Centu...	naaz0412	2024-01-03 19:41:57	6.0	Great trailer. Had my attention for sure.	Bubba_Blade	2024-01-03 23:15:46	7.0
357558	The First Omen	2024	The First Omen | Official Trailer | 20th Centu...	naaz0412	2024-01-03 19:41:57	6.0	Is she typecast now? First was the servant an...	messengers1	2024-01-04 07:43:02	3.0

357559 rows × 10 columns

print(data.isnull().sum())

Movie Title           3
Year                 12
Post Title            9
Post Author           9
Post Time            21
Post Score           21
Comment              26
Comment Author    62074
Comment Time         33
Comment Score        33
dtype: int64

2.2.2 Handle Missing Values

# Delete the missing rows in the core column
filtered_data1 = data.dropna(subset=['Comment', 'Comment Time', 'Movie Title', 'Year'])

# Check the amount of data remaining
print(f"amount of original data: {len(data)}, after cleaning: {len(filtered_data1)}")

amount of original data: 357559, after cleaning: 357521

# Keep the most liked comments
filtered_data2 =filtered_data1.loc[filtered_data1.groupby(['Comment Author', 'Movie Title'])['Comment Score'].idxmax()]

filtered_data2

	Movie Title	Year	Post Title	Post Author	Post Time	Post Score	Comment	Comment Author	Comment Time	Comment Score
101818	La La Land	2016	Just re-watched La La Land	Natergator97	2023-07-21 16:54:17	1786.0	I don't understand people who have a gripe wit...	--------rook	2023-07-22 01:08:52	8.0
91331	Allegiant	2016.0	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	98.0	Fuck it I'll just say it, Jeff Daniels was so ...	------Username------	2016-03-19 04:45:48	171.0
12349	Divergent	2014	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	104.0	Fuck it I'll just say it, Jeff Daniels was so ...	------Username------	2016-03-19 04:45:48	171.0
195259	Rampage	2018.0	Dwayne Johnson’s 'Rampage' Officially Begins F...	BunyipPouch	2017-04-17 16:39:56	300.0	So they're making this with a proper budget, b...	-----BroAway-----	2017-04-18 03:39:08	4.0
279643	Soul	2020	Pixar’s ‘Soul’ Release Delayed Until November	BunyipPouch	2020-04-13 18:04:30	2431.0	I really dont watch big block busters but seei...	-----godspeed-----	2020-04-13 20:06:58	9.0
...	...	...	...	...	...	...	...	...	...	...
229265	Terminator: Dark Fate	2019	'Terminator 2: Judgment Day' fan screening tur...	zzman1490	2019-10-21 02:48:59	47447.0	Just got back from one of the screenings, inte...	zzman1490	2019-10-21 02:52:32	5781.0
225958	Spider-Man: Into the Spider-Verse	2018	'Spider-Man: Into the Spider-Verse' Review Meg...	Sisiwakanamaru	2018-11-28 13:26:52	1765.0	Man, I expected this movie to be good, and I w...	zzuxon	2018-11-28 17:24:52	76.0
307748	Black Adam	2022	BLACK ADAM - Official Trailer 2	Turbostrider27	2022-09-08 23:45:49	440.0	This looks like dawn of the seven from the boys	zzz099	2022-09-09 15:06:12	3.0
48360	Amy	2015	First Images of Amy Adams in 'Nightbitch' - A ...	MarvelsGrantMan136	2024-07-18 15:09:10	2398.0	I really hope this is one of those movies that...	zzzzarf	2024-07-18 23:54:39	12.0
342303	Elemental	2023	Official Discussion: Elemental [SPOILERS]	mi-16evil	2023-06-16 02:02:55	657.0	This movie made me sob. As the eldest daughter...	zzzzooooiiiiinnnkkkk	2023-10-03 14:08:12	81.0

262005 rows × 10 columns

2.2.3 Data Transformation and Standardization

# 1. Standardize time formats
filtered_data2['Comment Time'] = pd.to_datetime(filtered_data2['Comment Time'])
filtered_data2['Post Time'] = pd.to_datetime(filtered_data2['Post Time'])

# 2. Extract comment year for subsequent analysis
filtered_data2['Comment Year'] = filtered_data2['Comment Time'].dt.year

# 3. Create new dataset filtered_data3 as a copy
filtered_data3 = filtered_data2.copy()  # Create copy to preserve original data

# Validation checks
print("First 5 rows after time standardization:")
print(filtered_data3[['Comment Time', 'Post Time', 'Comment Year']].head())

print("\nNon-null value counts:")
print(filtered_data3.count())

# Optional: Save to CSV
filtered_data3.to_csv('filtered_data3.csv', index=False)

# Additional quality checks
print("\nData types after conversion:")
print(filtered_data3.dtypes)

print("\nComment year range:")
print("From:", filtered_data3['Comment Year'].min(), 
      "To:", filtered_data3['Comment Year'].max())

print("\nNull value check:")
print(filtered_data3[['Comment Time', 'Post Time', 'Comment Year']].isnull().sum())

First 5 rows after time standardization:
              Comment Time           Post Time  Comment Year
101818 2023-07-22 01:08:52 2023-07-21 16:54:17          2023
91331  2016-03-19 04:45:48 2016-03-19 03:03:17          2016
12349  2016-03-19 04:45:48 2016-03-19 03:03:17          2016
195259 2017-04-18 03:39:08 2017-04-17 16:39:56          2017
279643 2020-04-13 20:06:58 2020-04-13 18:04:30          2020

Non-null value counts:
Movie Title       262005
Year              262005
Post Title        262005
Post Author       262005
Post Time         262005
Post Score        262005
Comment           262005
Comment Author    262005
Comment Time      262005
Comment Score     262005
Comment Year      262005
dtype: int64

Data types after conversion:
Movie Title               object
Year                      object
Post Title                object
Post Author               object
Post Time         datetime64[ns]
Post Score               float64
Comment                   object
Comment Author            object
Comment Time      datetime64[ns]
Comment Score            float64
Comment Year               int32
dtype: object

Comment year range:
From: 2014 To: 2024

Null value check:
Comment Time    0
Post Time       0
Comment Year    0
dtype: int64

# First convert Year column to float, then to integer
filtered_data3['Year'] = filtered_data3['Year'].astype(float).astype(int)

# Filter comments within target year range (2014-2024)
filtered_data4 = filtered_data3[
    (filtered_data3['Comment Year'] >= 2014) & 
    (filtered_data3['Comment Year'] <= 2024)
]

# Ensure comment year is not earlier than movie release year
filtered_data4 = filtered_data4[
    filtered_data4['Comment Year'] >= filtered_data4['Year']
]

# Validate the results
print("Data filtering summary:")
print(f"Original records: {len(filtered_data3)}")
print(f"Filtered records: {len(filtered_data4)}")
print(f"Records removed: {len(filtered_data3) - len(filtered_data4)}")

print("\nYear validation:")
print(f"Minimum release year: {filtered_data4['Year'].min()}")
print(f"Maximum comment year: {filtered_data4['Comment Year'].max()}")
print(f"Comments before movie release: {(filtered_data4['Comment Year'] < filtered_data4['Year']).sum()} records")

Data filtering summary:
Original records: 262005
Filtered records: 222792
Records removed: 39213

Year validation:
Minimum release year: 2014
Maximum comment year: 2024
Comments before movie release: 0 records

def clean_text(text):
    """Clean and preprocess text data by:
    1. Removing HTML tags
    2. Removing URLs
    3. Keeping basic punctuation"""
    # Remove HTML tags
    text = re.sub(r'<[^>]+>', '', text)
    # Remove URLs
    text = re.sub(r'http\S+', '', text)
    # Keep only basic punctuation (optional)
    text = re.sub(r'[^\w\s.,!?]', '', text)
    return text.strip()

# Apply text cleaning to comments
filtered_data4['Comment'] = filtered_data4['Comment'].apply(clean_text)

# Remove empty comments after cleaning
filtered_data5 = filtered_data4[filtered_data4['Comment'].str.len() > 0]

# Print cleaning results
print("Text cleaning summary:")
print(f"Original comments count: {len(filtered_data4)}")
print(f"After removing empty comments: {len(filtered_data5)}")
print(f"Empty comments removed: {len(filtered_data4) - len(filtered_data5)}")

# Optional: Show sample of cleaned comments
print("\nSample of cleaned comments:")
print(filtered_data5['Comment'].head())

Text cleaning summary:
Original comments count: 222792
After removing empty comments: 222520
Empty comments removed: 272

Sample of cleaned comments:
101818    I dont understand people who have a gripe with...
91331     Fuck it Ill just say it, Jeff Daniels was so f...
12349     Fuck it Ill just say it, Jeff Daniels was so f...
279643    I really dont watch big block busters but seei...
4451                            And so are the lens flares.
Name: Comment, dtype: object

# Check the count of non-null values in each column
print(filtered_data5.count())

# Check the range of years
print("Comment year range:", filtered_data5['Comment Year'].min(), "-", filtered_data5['Comment Year'].max())
print("Movie year range:", filtered_data5['Year'].min(), "-", filtered_data5['Year'].max())

Movie Title       222520
Year              222520
Post Title        222520
Post Author       222520
Post Time         222520
Post Score        222520
Comment           222520
Comment Author    222520
Comment Time      222520
Comment Score     222520
Comment Year      222520
dtype: int64
Comment year range: 2014 - 2024
Movie year range: 2014 - 2024

# Assign the processed dataset to new_data
comment_data = filtered_data5.copy()

# Output the information of new_data
print(comment_data.info())

<class 'pandas.core.frame.DataFrame'>
Index: 222520 entries, 101818 to 342303
Data columns (total 11 columns):
 #   Column          Non-Null Count   Dtype         
---  ------          --------------   -----         
 0   Movie Title     222520 non-null  object        
 1   Year            222520 non-null  int64         
 2   Post Title      222520 non-null  object        
 3   Post Author     222520 non-null  object        
 4   Post Time       222520 non-null  datetime64[ns]
 5   Post Score      222520 non-null  float64       
 6   Comment         222520 non-null  object        
 7   Comment Author  222520 non-null  object        
 8   Comment Time    222520 non-null  datetime64[ns]
 9   Comment Score   222520 non-null  float64       
 10  Comment Year    222520 non-null  int32         
dtypes: datetime64[ns](2), float64(2), int32(1), int64(1), object(5)
memory usage: 19.5+ MB
None

# Export CSV 
comment_data.to_csv('comment_data.csv', index=False, quoting=1) 

print("Dataset has been successfully exported as comment_data.csv")

Dataset has been successfully exported as comment_data.csv

comment_data

	Movie Title	Year	Post Title	Post Author	Post Time	Post Score	Comment	Comment Author	Comment Time	Comment Score	Comment Year
101818	La La Land	2016	Just re-watched La La Land	Natergator97	2023-07-21 16:54:17	1786.0	I dont understand people who have a gripe with...	--------rook	2023-07-22 01:08:52	8.0	2023
91331	Allegiant	2016	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	98.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016
12349	Divergent	2014	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	104.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016
279643	Soul	2020	Pixar’s ‘Soul’ Release Delayed Until November	BunyipPouch	2020-04-13 18:04:30	2431.0	I really dont watch big block busters but seei...	-----godspeed-----	2020-04-13 20:06:58	9.0	2020
4451	The Lego Movie	2014	In The Lego Movie, when master builders move q...	notamustache	2014-06-11 12:52:39	3144.0	And so are the lens flares.	----0----	2014-06-11 16:26:09	39.0	2014
...	...	...	...	...	...	...	...	...	...	...	...
229265	Terminator: Dark Fate	2019	'Terminator 2: Judgment Day' fan screening tur...	zzman1490	2019-10-21 02:48:59	47447.0	Just got back from one of the screenings, inte...	zzman1490	2019-10-21 02:52:32	5781.0	2019
225958	Spider-Man: Into the Spider-Verse	2018	'Spider-Man: Into the Spider-Verse' Review Meg...	Sisiwakanamaru	2018-11-28 13:26:52	1765.0	Man, I expected this movie to be good, and I w...	zzuxon	2018-11-28 17:24:52	76.0	2018
307748	Black Adam	2022	BLACK ADAM - Official Trailer 2	Turbostrider27	2022-09-08 23:45:49	440.0	This looks like dawn of the seven from the boys	zzz099	2022-09-09 15:06:12	3.0	2022
48360	Amy	2015	First Images of Amy Adams in 'Nightbitch' - A ...	MarvelsGrantMan136	2024-07-18 15:09:10	2398.0	I really hope this is one of those movies that...	zzzzarf	2024-07-18 23:54:39	12.0	2024
342303	Elemental	2023	Official Discussion: Elemental [SPOILERS]	mi-16evil	2023-06-16 02:02:55	657.0	This movie made me sob. As the eldest daughter...	zzzzooooiiiiinnnkkkk	2023-10-03 14:08:12	81.0	2023

222520 rows × 11 columns

2.2.4 Automated Quality Checks

import pandas as pd

comment_data_check = pd.read_csv('/Users/heshuqing0305/Downloads/comment_data.csv')

comment_data_check

	Movie Title	Year	Post Title	Post Author	Post Time	Post Score	Comment	Comment Author	Comment Time	Comment Score	Comment Year
0	La La Land	2016	Just re-watched La La Land	Natergator97	2023-07-21 16:54:17	1786.0	I dont understand people who have a gripe with...	--------rook	2023-07-22 01:08:52	8.0	2023
1	Allegiant	2016	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	98.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016
2	Divergent	2014	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	104.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016
3	Soul	2020	Pixar’s ‘Soul’ Release Delayed Until November	BunyipPouch	2020-04-13 18:04:30	2431.0	I really dont watch big block busters but seei...	-----godspeed-----	2020-04-13 20:06:58	9.0	2020
4	The Lego Movie	2014	In The Lego Movie, when master builders move q...	notamustache	2014-06-11 12:52:39	3144.0	And so are the lens flares.	----0----	2014-06-11 16:26:09	39.0	2014
...	...	...	...	...	...	...	...	...	...	...	...
222515	Terminator: Dark Fate	2019	'Terminator 2: Judgment Day' fan screening tur...	zzman1490	2019-10-21 02:48:59	47447.0	Just got back from one of the screenings, inte...	zzman1490	2019-10-21 02:52:32	5781.0	2019
222516	Spider-Man: Into the Spider-Verse	2018	'Spider-Man: Into the Spider-Verse' Review Meg...	Sisiwakanamaru	2018-11-28 13:26:52	1765.0	Man, I expected this movie to be good, and I w...	zzuxon	2018-11-28 17:24:52	76.0	2018
222517	Black Adam	2022	BLACK ADAM - Official Trailer 2	Turbostrider27	2022-09-08 23:45:49	440.0	This looks like dawn of the seven from the boys	zzz099	2022-09-09 15:06:12	3.0	2022
222518	Amy	2015	First Images of Amy Adams in 'Nightbitch' - A ...	MarvelsGrantMan136	2024-07-18 15:09:10	2398.0	I really hope this is one of those movies that...	zzzzarf	2024-07-18 23:54:39	12.0	2024
222519	Elemental	2023	Official Discussion: Elemental [SPOILERS]	mi-16evil	2023-06-16 02:02:55	657.0	This movie made me sob. As the eldest daughter...	zzzzooooiiiiinnnkkkk	2023-10-03 14:08:12	81.0	2023

222520 rows × 11 columns

def detect_common_issues(text_series):
    """Quantify common text quality issues"""
    results = {
        'contains_url': text_series.str.contains(r'http[s]?://').mean(),
        'html_tags': text_series.str.contains(r'<[^>]+>').mean(),
        'non_ascii': text_series.str.contains(r'[^\x00-\x7F]').mean(),
        'excessive_spaces': text_series.str.contains(r'\s{3,}').mean()
    }
    return pd.Series(results)

# Check initial quality metrics
quality_metrics = detect_common_issues(comment_data_check['Comment'])
print("\nInitial Quality Metrics:")
print(quality_metrics.apply(lambda x: f"{x:.1%}"))

# Replace multiple consecutive spaces with a single space
comment_data_check['Comment'] = comment_data_check['Comment'].str.replace(
    r'\s{3,}',  # Match three or more spaces
    ' ',        # Replace with a single space
    regex=True
)

# Re-check validation
quality_metrics_after = detect_common_issues(comment_data_check['Comment'])
print("\nQuality Metrics After Processing:")
print(quality_metrics_after.apply(lambda x: f"{x:.1%}"))

Initial Quality Metrics:
contains_url         0.0%
html_tags            0.0%
non_ascii            0.6%
excessive_spaces    10.0%
dtype: object

Quality Metrics After Processing:
contains_url        0.0%
html_tags           0.0%
non_ascii           0.6%
excessive_spaces    0.0%
dtype: object

# Define the function to remove non-ASCII characters
def remove_non_ascii(text):
    return text.encode('ascii', 'ignore').decode('ascii')

# Apply to the Comment column
comment_data_check['Comment'] = comment_data_check['Comment'].apply(remove_non_ascii)

# Validate results
non_ascii_remaining = comment_data_check['Comment'].str.contains(r'[^\x00-\x7F]').mean()
print(f"Proportion of non-ASCII characters after processing: {non_ascii_remaining:.2%}")  # Should display 0.00%

Proportion of non-ASCII characters after processing: 0.00%

# Create a new DataFrame with the processed data
comment_data_processed = comment_data_check.copy()

2.3 Text Enhancement Processing

comment_data_processed

	Movie Title	Year	Post Title	Post Author	Post Time	Post Score	Comment	Comment Author	Comment Time	Comment Score	Comment Year
0	La La Land	2016	Just re-watched La La Land	Natergator97	2023-07-21 16:54:17	1786.0	I dont understand people who have a gripe with...	--------rook	2023-07-22 01:08:52	8.0	2023
1	Allegiant	2016	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	98.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016
2	Divergent	2014	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	104.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016
3	Soul	2020	Pixar’s ‘Soul’ Release Delayed Until November	BunyipPouch	2020-04-13 18:04:30	2431.0	I really dont watch big block busters but seei...	-----godspeed-----	2020-04-13 20:06:58	9.0	2020
4	The Lego Movie	2014	In The Lego Movie, when master builders move q...	notamustache	2014-06-11 12:52:39	3144.0	And so are the lens flares.	----0----	2014-06-11 16:26:09	39.0	2014
...	...	...	...	...	...	...	...	...	...	...	...
222515	Terminator: Dark Fate	2019	'Terminator 2: Judgment Day' fan screening tur...	zzman1490	2019-10-21 02:48:59	47447.0	Just got back from one of the screenings, inte...	zzman1490	2019-10-21 02:52:32	5781.0	2019
222516	Spider-Man: Into the Spider-Verse	2018	'Spider-Man: Into the Spider-Verse' Review Meg...	Sisiwakanamaru	2018-11-28 13:26:52	1765.0	Man, I expected this movie to be good, and I w...	zzuxon	2018-11-28 17:24:52	76.0	2018
222517	Black Adam	2022	BLACK ADAM - Official Trailer 2	Turbostrider27	2022-09-08 23:45:49	440.0	This looks like dawn of the seven from the boys	zzz099	2022-09-09 15:06:12	3.0	2022
222518	Amy	2015	First Images of Amy Adams in 'Nightbitch' - A ...	MarvelsGrantMan136	2024-07-18 15:09:10	2398.0	I really hope this is one of those movies that...	zzzzarf	2024-07-18 23:54:39	12.0	2024
222519	Elemental	2023	Official Discussion: Elemental [SPOILERS]	mi-16evil	2023-06-16 02:02:55	657.0	This movie made me sob. As the eldest daughter...	zzzzooooiiiiinnnkkkk	2023-10-03 14:08:12	81.0	2023

222520 rows × 11 columns

import spacy
import pandas as pd
import re
from tqdm import tqdm
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import nltk
from collections import defaultdict
from nltk import bigrams, trigrams
from nltk.tokenize import word_tokenize

# Initialize NLP pipeline
print("Loading NLP models...")
nlp = spacy.load("en_core_web_lg", disable=["parser", "ner"])
lemmatizer = WordNetLemmatizer()

# Download NLTK resources
nltk.download('stopwords', quiet=True)
nltk.download('wordnet', quiet=True)
nltk.download('punkt', quiet=True)

class EnhancedTextPreprocessor:
    def __init__(self):
        # Pre-compiled patterns
        self.HTML_PATTERN = re.compile(r'<[^>]+>')
        self.URL_PATTERN = re.compile(r'https?://\S+|www\.\S+')
        self.EMOJI_PATTERN = re.compile("["
                              u"\U0001F600-\U0001F64F"
                              u"\U0001F300-\U0001F5FF"
                              u"\U0001F680-\U0001F6FF"
                              u"\U0001F1E0-\U0001F1FF"
                              "]+", flags=re.UNICODE)
        self.PUNCT_PATTERN = re.compile(r'[^\w\s\'-]|_')
        self.APOSTROPHE_PATTERN = re.compile(r"\s'|'\s|^'|'$")
        
        # Enhanced contraction handling
        self.CONTRACTIONS = [
            (re.compile(r"(\w+)n't\b", re.IGNORECASE), r"\1 not"),
            (re.compile(r"(\w+)'re\b", re.IGNORECASE), r"\1 are"),
            (re.compile(r"(\w+)'s\b", re.IGNORECASE), r"\1 is"),
            (re.compile(r"(\w+)'d\b", re.IGNORECASE), r"\1 would"),
            (re.compile(r"(\w+)'ll\b", re.IGNORECASE), r"\1 will"),
            (re.compile(r"(\w+)'ve\b", re.IGNORECASE), r"\1 have"),
            (re.compile(r"(\w+)'m\b", re.IGNORECASE), r"\1 am")
        ]
        
        # Linguistic resources
        self.stop_words = self._init_stopwords()
        self.protected_terms = self._init_protected_terms()
        self.cinema_terms = self._init_cinema_terms()
        self.common_phrases = self._init_common_phrases()
        self.technical_verbs = self._init_technical_verbs()
        
        # Build combined term patterns (prioritize longer phrases)
        all_terms = (self.protected_terms.keys() | 
                    self.cinema_terms | 
                    self.common_phrases |
                    {k.replace('_', ' ') for k in self.protected_terms.values()})
        
        self.term_patterns = sorted([
            (re.compile(rf'(?<!\w){re.escape(term)}(?!\w)', re.IGNORECASE), 
             f"[[{term.replace(' ', '_')}]]")
            for term in all_terms
        ], key=lambda x: -len(x[0].pattern))
        
        # Technical term processing rules
        self.tech_token_rules = {
            **{k: v for k, v in {
                'vfx': 'visual_effects',
                'cgi': 'computer_generated_imagery',
                'fx': 'special_effects',
                '3d': '3d_technology',
                'hdr': 'high_dynamic_range',
                'dolby': 'dolby_atmos',
                'imax': 'imax',
                'rotoscope': 'rotoscoping',
                'matte': 'matte_painting'
            }.items()},
            **{v.split('_')[0]: v for v in self.protected_terms.values()}
        }
        
        # POS tags to retain
        self.keep_pos = {'NOUN', 'PROPN', 'ADJ', 'VERB', 'ADV'}
    
    def _init_stopwords(self):
        """Enhanced stopwords list preserving sentiment words"""
        base_stopwords = set(stopwords.words('english')) - {
            'not', 'no', 'never', 'very', 'too', 'against', 'only'
        }
        
        custom_stops = {
            "movie", "film", "like", "just", "say", "go", 
            "get", "make", "see", "know", "think", "look"
        }
        return base_stopwords | custom_stops
    
    def _init_protected_terms(self):
        """Domain-specific terms to protect"""
        return {
            "mcu": "marvel_cinematic_universe",
            "dceu": "dc_extended_universe",
            "imax": "imax",
            "4k": "4k_resolution",
            "avengers": "avengers",
            "xmen": "x_men",
            "dolby": "dolby_atmos",
            "hdr": "high_dynamic_range",
            "star wars": "star_wars",
            "lord of the rings": "lord_of_the_rings",
            "harry potter": "harry_potter",
            "pixar": "pixar",
            "disney": "disney"
        }
    
    def _init_cinema_terms(self):
        """Cinema technical terms dictionary"""
        return {
            "visual effects", "special effects", "computer generated imagery",
            "frame rate", "color grading", "sound mixing", "aspect ratio",
            "steady cam", "green screen", "motion capture", "foley",
            "stop motion", "rotoscoping", "matte painting", "cinematography",
            "depth of field", "lighting ratio", "chroma key", "digital intermediate"
        }
    
    def _init_technical_verbs(self):
        """Film production related verbs"""
        return {
            "filming", "lighting", "grading", "rendering", "compositing",
            "tracking", "animating", "keying", "sequencing", "editing"
        }
    
    def _init_common_phrases(self):
        """Frequently used movie discussion phrases"""
        return {
            "box office", "opening weekend", "post credit scene",
            "director's cut", "special edition", "theatrical release",
            "streaming platform", "film franchise", "cinematic universe",
            "blu ray", "4k remaster", "film noir", "character arc",
            "plot twist", "supporting actor", "leading role", "ensemble cast",
            "visual effects supervisor", "special effects team"
        }
    
    def _protect_technical_terms(self, text):
        """Preserve technical terms (prioritize longer phrases)"""
        for pattern, repl in self.term_patterns:
            text = pattern.sub(repl, text)
        return text
    
    def _handle_technical_verbs(self, token):
        """Process technical verbs"""
        if token.lemma_ in self.technical_verbs:
            return token.lemma_ + "_technique"
        return None
    
    def _extract_ngrams(self, tokens):
        """Extract meaningful n-gram phrases"""
        protected_phrases = set()
        
        # Process technical verb combinations (e.g. "color grading")
        for i in range(len(tokens)-1):
            bigram = f"{tokens[i]} {tokens[i+1]}"
            if bigram in self.cinema_terms:
                protected_phrases.add(bigram.replace(' ', '_'))
        
        # Add predefined phrases
        for phrase in self.common_phrases:
            if phrase.replace(' ', '_') in ' '.join(tokens):
                protected_phrases.add(phrase.replace(' ', '_'))
                
        return list(protected_phrases)
    
    def process_token(self, token):
        """Enhanced token processing pipeline"""
        # Skip punctuation/space/short tokens
        if token.is_punct or token.is_space or len(token.text) < 2:
            return None
            
        # Check protected terms
        lower_text = token.text.lower()
        if lower_text in self.protected_terms:
            return self.protected_terms[lower_text]
            
        # Handle technical abbreviations
        if lower_text in self.tech_token_rules:
            return self.tech_token_rules[lower_text]
            
        # Process technical verbs
        tech_verb = self._handle_technical_verbs(token)
        if tech_verb:
            return tech_verb
            
        # Stopword filtering
        if lower_text in self.stop_words:
            return None
            
        # Number processing
        if token.like_num:
            if len(token.text) == 4 and token.text.isdigit():
                return f"YEAR_{token.text}"
            return None
            
        # POS filtering
        if token.pos_ not in self.keep_pos:
            return None
            
        # Lemmatization
        if token.pos_ == 'VERB':
            return lemmatizer.lemmatize(lower_text, pos='v')
        elif token.pos_ in ('NOUN', 'PROPN'):
            base = lemmatizer.lemmatize(lower_text)
            return base if base in self.protected_terms else lower_text
        return lower_text
    
    def preprocess_text(self, text):
        """Complete preprocessing pipeline"""
        if not isinstance(text, str) or not text.strip():
            return ""
            
        text = str(text).strip()
        
        # Stage 1: Basic cleaning
        text = self.HTML_PATTERN.sub('', text)
        text = self.URL_PATTERN.sub('', text)
        text = self.EMOJI_PATTERN.sub('', text)
        
        # Stage 2: Contraction handling
        for pattern, repl in self.CONTRACTIONS:
            text = pattern.sub(repl, text)
        
        # Stage 3: Term protection
        text = self._protect_technical_terms(text)
        
        # Stage 4: Punctuation handling
        text = self.PUNCT_PATTERN.sub(' ', text)
        text = text.lower()
        
        # Stage 5: Linguistic processing
        doc = nlp(text)
        tokens = []
        for token in doc:
            processed = self.process_token(token)
            if processed:
                tokens.append(processed)
        
        # Stage 6: N-gram detection
        ngrams = self._extract_ngrams(tokens)
        processed_text = " ".join(tokens + ngrams)
        
        # Final cleanup
        processed_text = re.sub(r'\s+', ' ', processed_text).strip()
        return processed_text

def process_in_batches(df, text_column='Comment', batch_size=5000):
    """
    Memory-optimized batch processing with progress tracking
    Args:
        df: Input DataFrame
        text_column: Column containing raw text
        batch_size: Number of records per batch
    Returns:
        Series with processed texts
    """
    processor = EnhancedTextPreprocessor()
    results = []
    
    for i in tqdm(range(0, len(df), batch_size),
                 desc=f"Processing {len(df)} rows"):
        batch = df.iloc[i:i+batch_size]
        processed = [processor.preprocess_text(t) for t in batch[text_column]]
        results.extend(processed)
    
    return pd.Series(results, index=df.index)

def validate_with_test_cases():
    """Comprehensive validation suite"""
    test_cases = [
        ("I've watched Avengers: Endgame in IMAX 4K!", 
         "have watch avengers_endgame imax 4k_resolution"),
        ("The DCEU movies aren't better than MCU's VFX", 
         "dc_extended_universe movies not better marvel_cinematic_universe visual_effects"),
        ("The box office results for opening weekend were amazing", 
         "box_office opening_weekend amazing"),
        ("This CGI doesn't make sense but the 3D effects are good", 
         "computer_graphics not make sense 3d_technology effects good"),
        ("Filmed in 2018 with 120fps steady cam", 
         "film YEAR_2018 120fps steady_cam"),
        ("Character arc was weak despite good cinematography", 
         "character_arc weak despite good cinematography"),
        ("The post credit scene hinted at XMen coming to MCU", 
         "post_credit_scene hint x_men come marvel_cinematic_universe")
    ]
    
    processor = EnhancedTextPreprocessor()
    print("\n=== Validation Results ===")
    for original, expected in test_cases:
        result = processor.preprocess_text(original)
        status = "✅" if result == expected else "❌"
        print(f"{status} Input: {original}")
        print(f"   Expected: {expected}")
        print(f"   Actual:   {result}\n")

if __name__ == '__main__':
    print("Starting enhanced text preprocessing...")
    validate_with_test_cases()

    # Process data
    comment_data_processed['processed_text'] = process_in_batches(comment_data_processed)
    
    # Save results
    output_path = 'enhanced_preprocessed_comments.csv'
    comment_data_processed.to_csv(output_path, index=False)
    print(f"\nProcessing completed. Results saved to {output_path}")
    
    # Run validation
    validate_with_test_cases()
    
    # Show samples
    print("\n=== Processed Samples ===")
    samples = comment_data_processed[['Comment', 'processed_text']].sample(3)
    for _, row in samples.iterrows():
        print(f"Original: {row['Comment']}")
        print(f"Processed: {row['processed_text']}\n")

Loading NLP models...
Starting enhanced text preprocessing...

=== Validation Results ===
❌ Input: I've watched Avengers: Endgame in IMAX 4K!
   Expected: have watch avengers_endgame imax 4k_resolution
   Actual:   watch avengers endgame imax 4k_resolution

❌ Input: The DCEU movies aren't better than MCU's VFX
   Expected: dc_extended_universe movies not better marvel_cinematic_universe visual_effects
   Actual:   dc_extended_universe movies better marvel_cinematic_universe visual_effects cinematic_universe

❌ Input: The box office results for opening weekend were amazing
   Expected: box_office opening_weekend amazing
   Actual:   box office results opening weekend amazing

❌ Input: This CGI doesn't make sense but the 3D effects are good
   Expected: computer_graphics not make sense 3d_technology effects good
   Actual:   computer_generated_imagery sense 3d_technology effects good

❌ Input: Filmed in 2018 with 120fps steady cam
   Expected: film YEAR_2018 120fps steady_cam
   Actual:   film YEAR_2018 steady cam steady_cam

❌ Input: Character arc was weak despite good cinematography
   Expected: character_arc weak despite good cinematography
   Actual:   character arc weak good cinematography

❌ Input: The post credit scene hinted at XMen coming to MCU
   Expected: post_credit_scene hint x_men come marvel_cinematic_universe
   Actual:   post credit scene hint x_men come marvel_cinematic_universe cinematic_universe

Processing 222520 rows: 100%|███████████████████| 45/45 [11:34<00:00, 15.44s/it]

Processing completed. Results saved to enhanced_preprocessed_comments.csv

=== Validation Results ===
❌ Input: I've watched Avengers: Endgame in IMAX 4K!
   Expected: have watch avengers_endgame imax 4k_resolution
   Actual:   watch avengers endgame imax 4k_resolution

❌ Input: The DCEU movies aren't better than MCU's VFX
   Expected: dc_extended_universe movies not better marvel_cinematic_universe visual_effects
   Actual:   dc_extended_universe movies better marvel_cinematic_universe visual_effects cinematic_universe

❌ Input: The box office results for opening weekend were amazing
   Expected: box_office opening_weekend amazing
   Actual:   box office results opening weekend amazing

❌ Input: This CGI doesn't make sense but the 3D effects are good
   Expected: computer_graphics not make sense 3d_technology effects good
   Actual:   computer_generated_imagery sense 3d_technology effects good

❌ Input: Filmed in 2018 with 120fps steady cam
   Expected: film YEAR_2018 120fps steady_cam
   Actual:   film YEAR_2018 steady cam steady_cam

❌ Input: Character arc was weak despite good cinematography
   Expected: character_arc weak despite good cinematography
   Actual:   character arc weak good cinematography

❌ Input: The post credit scene hinted at XMen coming to MCU
   Expected: post_credit_scene hint x_men come marvel_cinematic_universe
   Actual:   post credit scene hint x_men come marvel_cinematic_universe cinematic_universe


=== Processed Samples ===
Original: All these shaky camera movies set in a house run together in my mind. I cant even remember what The Conjuring was about. I dont even know if Ive seen Conjuring 2 or not. These shaky horror movies with the black and white scenes in bedrooms with people sleeping in bed for ten minutes are all the same. And then a door swings open. Oh, big deal, Im so fucking afraid.
Processed: shaky camera movies set house run together mind even remember conjuring even see conjure shaky horror movies black white scenes bedrooms people sleep bed minutes door swing open big deal fuck afraid

Original: Can we let actors do their job before we place hate on them.
Processed: let actors job place hate

Original: Its an excellent movie. I saw it at the local indie theater, but it is probably the most solid crowdpleasing thriller Ive seen in years, and Im amazed they didnt give it a wider release.

I expect its gonna explode when it hits Netflix or whatever. And is probably destined for an American remake.
Processed: excellent saw local indie theater probably solid crowdpleasing thriller see years amazed give wider release expect gon explode hit netflix probably destine american remake

comment_data_final = comment_data_processed.copy()

# Display original vs processed samples
pd.set_option('display.max_colwidth', 200)
sample_comparison = comment_data_final[['Comment', 'processed_text']].sample(10, random_state=42)
sample_comparison

	Comment	processed_text
36666	Film critic Locke Peterseim reviews 300 Rise of an Empire. The film provides a mirror in which we can see the dark aspects of ourselves and our culture. 300 and its sequel show how the big screen ...	critic locke peterseim review rise empire provide mirror dark aspects culture sequel show big screen illuminate darkness minds set halfhistorical facts beautiful vistas fantasy war battle salamis ...
42947	When Harry met sally	harry_potter meet sally
148891	Came for Ainz sama, got Fitz instead!	come ainz sama get fitz instead
214054	Lots of romcoms How to Lose a Guy, The Proposal, What Happens in Vegas	lots romcoms lose guy proposal happen vegas
149778	Get away from her you bitch, bitch	away bitch bitch
178560	Can definitely tell which scene was NOT lit by Kubrick.	definitely tell scene light kubrick
97105	A main difference is that Mission Impossible actually filmed in Rome, while Fast X looked like the entire movie was filmed on green screens for each actor the worst being the reveal of a certain s...	main difference mission impossible actually film rome fast look entire film green screens actor worst reveal certain come submarine literally look family member record iphone submit director
76634	What are some screenwriting tips for someone wanting to write horror?	screenwriting tips want write horror
22138	List of songs Spidey did on his Christmas Album Spidey, Its Cold Outside Swingin Around The Mistletoe Silent Night Youre Welcome Joy To The World That I Just Saved Spidey The Snowman Its Beginning...	list songs spidey christmas album spidey cold swingin mistletoe silent night welcome joy world save spidey snowman beginning lot nondenominational holiday ave maria
203476	Yup. Ma DongSeok, though since starring in the eternals he goes by Don Lee. Fantastic actor with loads of charisma. I first saw him in The Good, The Bad, and The Weird as the big silent hammer guy.	dongseok star eternals go lee fantastic actor loads charisma saw good bad weird big silent hammer guy

from collections import Counter

def analyze_processing(df):
    # Token length distribution
    token_lengths = df['processed_text'].str.split().str.len()
    print(f"Avg tokens per text: {token_lengths.mean():.1f}")
    print(f"Token length distribution:\n{token_lengths.describe()}")
    
    # Top vocabulary analysis
    all_tokens = [word for text in df['processed_text'] for word in text.split()]
    print("\nTop 20 frequent terms:")
    print(Counter(all_tokens).most_common(20))
    
    # Empty text check
    empty_count = df['processed_text'].str.strip().eq('').sum()
    print(f"\nEmpty outputs: {empty_count}/{len(df)} ({empty_count/len(df):.1%})")

analyze_processing(comment_data_final)

Avg tokens per text: 15.1
Token length distribution:
count    222520.000000
mean         15.118160
std          24.258997
min           0.000000
25%           5.000000
50%           9.000000
75%          17.000000
max         839.000000
Name: processed_text, dtype: float64

Top 20 frequent terms:
[('really', 27639), ('good', 22599), ('watch', 20613), ('make', 19983), ('love', 19787), ('go', 19532), ('movies', 19183), ('get', 18326), ('time', 16584), ('people', 16144), ('look', 15817), ('great', 15359), ('much', 14227), ('also', 13825), ('see', 13811), ('even', 13626), ('want', 12570), ('think', 12529), ('very', 12076), ('come', 11823)]

Empty outputs: 1336/222520 (0.6%)

2.4 Topic Modeling Methodology_NMF with TF-IDF (Abandoned）

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from scipy import sparse
import matplotlib.pyplot as plt
from tqdm import tqdm

# 1. Data validation
print("Data validation:")
print(f"Total records: {len(comment_data_final)}")
print(f"Empty text count: {comment_data_final['processed_text'].isna().sum()}")
print("\nSample data:")
print(comment_data_final[['Comment', 'processed_text']].head(2))

# 2. Initialize TF-IDF vectorizer
tfidf = TfidfVectorizer(
    max_features=10000,           # Limit the number of features (adjust based on memory)
    min_df=50,                   # Must appear in at least 20 documents
    max_df=0.6,                  # Ignore words that appear in more than 70% of documents
    ngram_range=(1, 3),          # Include unigrams and bigrams
    stop_words='english',        # Use built-in English stop words
    sublinear_tf=False,           # Do not use 1 + log(tf) scaling
    norm='l2',                   # Euclidean normalization
    smooth_idf=True,
    token_pattern=r'(?u)\b[^\d\W][^\d\W]+\b'# Prevent division by zero errors
)

# 3. Build TF-IDF matrix
print("\nStarting to build TF-IDF matrix...")
tfidf_matrix = tfidf.fit_transform(
    tqdm(comment_data_final['processed_text'], desc='Processing Texts')
)
print(f"\nGenerated matrix dimensions: {tfidf_matrix.shape} (documents, features)")

# 4. Get feature names
feature_names = tfidf.get_feature_names_out()
print("\nExample of first 20 features:")
print(feature_names[:20])

# 5. Key term validation
def check_term(term):
    try:
        idx = tfidf.vocabulary_[term]
        print(f"Term '{term}' exists (ID: {idx}, document frequency: {tfidf_matrix[:, idx].count_nonzero()})")
    except KeyError:
        print(f"Term '{term}' does not exist")

print("\nKey term checks:")
for term in ['imax', 'mcu', 'dceu', 'avengers', '4k']:
    check_term(term)

# 6. Visualize feature weight distribution
plt.figure(figsize=(10, 6))
plt.hist(tfidf_matrix.data, bins=100, log=True)
plt.title("TF-IDF Weight Distribution")
plt.xlabel("Weight Value")
plt.ylabel("Frequency (Log Scale)")
plt.show()

# 7. Save results
print("\nSaving results...")
# Save the matrix
sparse.save_npz('tfidf_matrix.npz', tfidf_matrix)
# Save feature mapping
pd.DataFrame({
    'feature_id': range(len(feature_names)),
    'feature_name': feature_names,
    'document_frequency': np.array(tfidf_matrix.astype(bool).sum(axis=0))[0]
}).to_csv('tfidf_features.csv', index=False)

# 8. Validate loading functionality
print("\nValidating file loadability...")
loaded_matrix = sparse.load_npz('tfidf_matrix.npz')
print(f"Loaded matrix dimensions: {loaded_matrix.shape} (should match previous)")
assert loaded_matrix.shape == tfidf_matrix.shape

print("\n=== TF-IDF Matrix construction completed ===")
print("Output files:")
print("- tfidf_matrix.npz (main matrix)")
print("- tfidf_features.csv (feature metadata)")

Data validation:
Total records: 222520
Empty text count: 0

Sample data:
                                                                                                                                                                                                   Comment  \
0  I dont understand people who have a gripe with the ending, because its my favorite part. Its about the people you meet and make connections and have experiences with along the way as you try to ac...   
1  Fuck it Ill just say it, Jeff Daniels was so fucking bad in this. That one scene towards the end where she blows up that machine and saves everyone, his little gasp of shock...... What in the fuck...   

                                                                                                                                                                                            processed_text  
0  understand people gripe ending favorite part people meet connections experiences way try achieve dreams others live integral moments remember even stay together people mean lives particular point ...  
1                                                                                                                     fuck ill jeff daniels fucking bad scene end blow machine save little gasp shock fuck  

Starting to build TF-IDF matrix...

Processing Texts: 100%|██████████████| 222520/222520 [00:03<00:00, 68266.45it/s]

Generated matrix dimensions: (222520, 8249) (documents, features)

Example of first 20 features:
['aaron' 'abandon' 'abilities' 'ability' 'able' 'able watch' 'abomination'
 'abrams' 'abrupt' 'absence' 'absent' 'absolute' 'absolute favorite'
 'absolutely' 'absolutely adore' 'absolutely amazing'
 'absolutely brilliant' 'absolutely fantastic' 'absolutely gorgeous'
 'absolutely incredible']

Key term checks:
Term 'imax' exists (ID: 3533, document frequency: 813)
Term 'mcu' does not exist
Term 'dceu' does not exist
Term 'avengers' exists (ID: 482, document frequency: 595)
Term '4k' does not exist

Saving results...

Validating file loadability...
Loaded matrix dimensions: (222520, 8249) (should match previous)

=== TF-IDF Matrix construction completed ===
Output files:
- tfidf_matrix.npz (main matrix)
- tfidf_features.csv (feature metadata)

# Print the shape of the TF-IDF matrix
print(tfidf_matrix.shape)  # This shows the number of documents and features in the matrix

(222520, 8249)

# Print the first 200 feature names
print(feature_names[:200])  # This displays the first 200 terms that were extracted as features

['aaron' 'abandon' 'abilities' 'ability' 'able' 'able watch' 'abomination'
 'abrams' 'abrupt' 'absence' 'absent' 'absolute' 'absolute favorite'
 'absolutely' 'absolutely adore' 'absolutely amazing'
 'absolutely brilliant' 'absolutely fantastic' 'absolutely gorgeous'
 'absolutely incredible' 'absolutely kill' 'absolutely love'
 'absolutely stunning' 'absolutely terrible' 'absorb' 'abstract' 'absurd'
 'absurdity' 'absurdly' 'abuse' 'abusive' 'abysmal' 'abyss' 'academy'
 'academy award' 'academy awards' 'accent' 'accents' 'accept' 'acceptable'
 'acceptance' 'access' 'accessible' 'accident' 'accidentally' 'acclaim'
 'accompany' 'accomplish' 'accord' 'account' 'accountant' 'accounts'
 'accuracy' 'accurate' 'accurately' 'accuse' 'achieve' 'achievement'
 'acid' 'acknowledge' 'acquire' 'act' 'act really' 'acting' 'acting good'
 'acting great' 'action' 'action comedy' 'action films' 'action flick'
 'action great' 'action hero' 'action movies' 'action pack' 'action scene'
 'action scenes' 'action sequence' 'action sequences' 'action set'
 'action star_wars' 'actions' 'active' 'actively' 'activity' 'actor'
 'actor play' 'actors' 'actors play' 'actress' 'actress play' 'actresses'
 'acts' 'actual' 'actual story' 'actually' 'actually bad' 'actually care'
 'actually enjoy' 'actually good' 'actually happen' 'actually kind'
 'actually like' 'actually look' 'actually love' 'actually make'
 'actually pretty' 'actually pretty good' 'actually quite'
 'actually really' 'actually really enjoy' 'actually saw' 'actually think'
 'actually want' 'actually watch' 'actually work' 'ad' 'ad astra' 'adam'
 'adam driver' 'adam mckay' 'adam sandler' 'adams' 'adapt' 'adaptation'
 'adaptations' 'adaption' 'add' 'addict' 'addiction' 'addition'
 'additional' 'address' 'adjust' 'admire' 'admission' 'admit' 'admittedly'
 'adonis' 'adopt' 'adorable' 'adore' 'ads' 'adult' 'adults' 'advance'
 'advanced' 'advanced screening' 'advantage' 'adventure' 'adventures'
 'advertise' 'advertisement' 'advertising' 'advice' 'advise' 'advocate'
 'aesthetic' 'aesthetics' 'af' 'affair' 'affairs' 'affect' 'affleck'
 'afflecks' 'afford' 'afraid' 'africa' 'african' 'afterlife' 'aftermath'
 'afternoon' 'agatha' 'age' 'age ultron' 'aged' 'agency' 'agenda' 'agent'
 'agents' 'ages' 'aggressive' 'aggressively' 'ago' 'ago really'
 'ago think' 'agree' 'ahead' 'ahead time' 'ai' 'aid' 'aim' 'air'
 'airplane' 'airport' 'aka' 'akin' 'akira' 'al' 'aladdin' 'alamo']

# Calculate sparsity of the TF-IDF matrix
non_zero_elements = tfidf_matrix.count_nonzero()  # Count the number of non-zero elements in the matrix
total_elements = tfidf_matrix.shape[0] * tfidf_matrix.shape[1]  # Calculate the total number of elements in the matrix
sparsity = 1 - (non_zero_elements / total_elements)  # Calculate the sparsity ratio
print(f"Sparsity ratio: {sparsity:.2%}")  # Print the sparsity ratio as a percentage

# Check for the existence of specific key terms in the TF-IDF matrix
for term in ['imax', 'mcu', 'dceu', 'avengers', '4k']:
    check_term(term)  # This function checks if the term exists and prints its ID and document frequency

Sparsity ratio: 99.86%
Term 'imax' exists (ID: 3533, document frequency: 813)
Term 'mcu' does not exist
Term 'dceu' does not exist
Term 'avengers' exists (ID: 482, document frequency: 595)
Term '4k' does not exist

import pandas as pd
import numpy as np
from tqdm import tqdm
from sklearn.decomposition import NMF
import matplotlib.pyplot as plt
import os
from collections import defaultdict

# 1. Data Preprocessing - Adding Temporal Information
print("Extracting temporal metadata...")

# Ensure data is loaded
assert 'comment_data_final' in globals(), "comment_data_final data is not loaded"

# Add comment year and movie period
comment_data_final['comment_year'] = pd.to_datetime(comment_data_final['Comment Time']).dt.year
comment_data_final['movie_period'] = comment_data_final['Year'].apply(
    lambda x: f"{int(x)-2}-{int(x)+2}" if pd.notnull(x) else "N/A"
)

# 2. Enhanced Yearly Topic Analyzer
class EnhancedYearlyTopicAnalyzer:
    def __init__(self, n_topics=8, random_state=42, min_docs=100):
        """
        Enhanced temporal topic analyzer
        
        Parameters:
            n_topics: Number of topics for each yearly group
            random_state: Random seed
            min_docs: Minimum number of documents to execute analysis
        """
        self.n_topics = n_topics
        self.random_state = random_state
        self.min_docs = min_docs
        self.models = {}
        self.year_stats = {}
        self.period_coverage = defaultdict(list)
        
    def _calculate_year_stats(self, df):
        """Calculate movie year statistics for each comment year"""
        stats = {}
        for year, group in df.groupby('comment_year'):
            movie_years = group['Year'].dropna().astype(int)
            if len(movie_years) > 0:
                stats[year] = {
                    'movie_range': f"{movie_years.min()}-{movie_years.max()}",
                    'median_year': int(movie_years.median()),
                    'period_coverage': group['movie_period'].value_counts().to_dict()
                }
            else:
                stats[year] = {
                    'movie_range': "N/A",
                    'median_year': None,
                    'period_coverage': {}
                }
        return stats
    
    def analyze_yearly_topics(self, tfidf_matrix, feature_names, df):
        """Execute temporal topic analysis"""
        print("\n=== Starting Enhanced Yearly Topic Analysis ===")
        
        # Calculate year statistics
        self.year_stats = self._calculate_year_stats(df)
        
        # Initialize storage
        yearly_topics = {}
        yearly_dfs = {}
        
        # Process by comment year
        year_groups = df.groupby('comment_year')
        
        for year, group in tqdm(year_groups, desc="Processing Years"):
            year_mask = df['comment_year'] == year
            year_matrix = tfidf_matrix[year_mask.values]
            
            # Only process years with enough documents
            if year_matrix.shape[0] >= self.min_docs:
                try:
                    # Train NMF model
                    nmf = NMF(n_components=self.n_topics, 
                              random_state=self.random_state,
                              max_iter=500)  # Increase iterations to ensure convergence
                    W = nmf.fit_transform(year_matrix)
                    H = nmf.components_
                    
                    # Store results
                    self.models[year] = nmf
                    yearly_topics[year] = self._extract_topics(H, feature_names)
                    yearly_dfs[year] = pd.DataFrame(W, 
                                                    index=group.index,
                                                    columns=[f"Topic_{i}" for i in range(self.n_topics)])
                    
                    # Record movie period coverage
                    for period, count in self.year_stats[year]['period_coverage'].items():
                        self.period_coverage[period].append(year)
                except Exception as e:
                    print(f"\nError processing year {year}: {str(e)}")
                    continue
        
        return yearly_topics, yearly_dfs
    
    def _extract_topics(self, components, feature_names, n_top_words=10):
        """Extract top words for each topic"""
        topics = []
        for topic_idx in range(components.shape[0]):
            top_indices = components[topic_idx].argsort()[:-n_top_words-1:-1]
            topics.append([feature_names[i] for i in top_indices])
        return topics
    
    def generate_report(self, yearly_topics):
        """Generate formatted temporal analysis report"""
        report = []
        for year in sorted(yearly_topics.keys()):
            stats = self.year_stats.get(year, {})
            report.append(f"\n=== {year} ===")
            report.append(f"Movie Year Range: {stats.get('movie_range', 'N/A')}")
            report.append(f"Median Movie Year: {stats.get('median_year', 'N/A')}")
            report.append("Top Movie Periods:")
            for period, count in sorted(stats.get('period_coverage', {}).items(), 
                                      key=lambda x: -x[1])[:3]:
                report.append(f"  - {period}: {count} comments")
            
            report.append("\nTopics:")
            for topic_idx, terms in enumerate(yearly_topics[year]):
                report.append(f"Topic {topic_idx + 1}: {', '.join(terms)}")
        
        return '\n'.join(report)
    
    def visualize_trends(self, yearly_topics, output_dir="yearly_analysis"):
        """Generate visualizations"""
        os.makedirs(output_dir, exist_ok=True)
        
        # 1. Number of topics per year
        plt.figure(figsize=(12, 6))
        years = sorted(yearly_topics.keys())
        plt.bar(years, [len(yearly_topics[y]) for y in years])
        plt.title("Number of Topics per Year")
        plt.xlabel("Year")
        plt.ylabel("Topic Count")
        plt.xticks(years, rotation=45)
        plt.tight_layout()
        plt.savefig(f"{output_dir}/yearly_topic_counts.png")
        plt.close()
        
        # 2. Movie period coverage heatmap
        period_years = {p: set(y) for p, y in self.period_coverage.items() if p != "N/A"}
        if period_years:
            periods = sorted(period_years.keys(), 
                            key=lambda x: int(x.split('-')[0]))
            all_years = sorted(set.union(*period_years.values()))
            
            # Create heatmap matrix
            heatmap = np.zeros((len(periods), len(all_years)))
            for i, p in enumerate(periods):
                for j, y in enumerate(all_years):
                    heatmap[i,j] = y in period_years[p]
            
            plt.figure(figsize=(15, 8))
            plt.imshow(heatmap, cmap='Blues', aspect='auto')
            plt.title("Movie Period Coverage by Comment Year")
            plt.xlabel("Comment Year")
            plt.ylabel("Movie Period")
            plt.yticks(range(len(periods)), periods)
            plt.xticks(range(len(all_years)), all_years, rotation=45)
            plt.colorbar(label="Coverage (1=Present)")
            plt.tight_layout()
            plt.savefig(f"{output_dir}/period_coverage_heatmap.png")
            plt.close()

# 3. Execute the analysis
print("\nInitializing enhanced temporal analysis...")
analyzer = EnhancedYearlyTopicAnalyzer(
    n_topics=8,
    random_state=42,
    min_docs=100  # At least 100 comments per year for analysis
)

yearly_topics, yearly_dfs = analyzer.analyze_yearly_topics(
    tfidf_matrix, 
    feature_names,
    comment_data_final
)

# 4. Generate output
print("\nGenerating reports and visualizations...")
temporal_report = analyzer.generate_report(yearly_topics)
print(temporal_report)

# 5. Save results
output_dir = "yearly_analysis"
os.makedirs(output_dir, exist_ok=True)

# Save yearly topic distributions
print("\nSaving yearly topic data...")
for year, df in yearly_dfs.items():
    df.to_csv(f"{output_dir}/{year}_topic_distributions.csv", index=False)
    
# Save report
with open(f"{output_dir}/yearly_topic_report.txt", "w") as f:
    f.write(temporal_report)

# Generate visualizations
analyzer.visualize_trends(yearly_topics, output_dir)

print("\n=== Enhanced Temporal Analysis Complete ===")
print("Output files:")
print(f"- {output_dir}/yearly_topic_report.txt (detailed topics by year)")
print(f"- {output_dir}/ (CSVs of document-topic distributions)")
print(f"- {output_dir}/yearly_topic_counts.png (visualization)")
print(f"- {output_dir}/period_coverage_heatmap.png (movie period coverage)")

Extracting temporal metadata...

Initializing enhanced temporal analysis...

=== Starting Enhanced Yearly Topic Analysis ===

Processing Years: 100%|█████████████████████████| 11/11 [00:06<00:00,  1.61it/s]

Generating reports and visualizations...

=== 2014 ===
Movie Year Range: 2014-2014
Median Movie Year: 2014
Top Movie Periods:
  - 2012-2016: 10620 comments

Topics:
Topic 1: love, make, movies, watch, great, think, people, time, saw, end
Topic 2: look, look good, look forward, forward, look great, look pretty, fun, look really, look fun, look terrible
Topic 3: trailer, watch trailer, watch, trailer make, trailer look, song, new, saw trailer, entire, teaser
Topic 4: fuck, fuck love, shit, fuck wait, fucking, holy, north korea, cast, korea, north
Topic 5: good, look good, pretty, pretty good, really good, leave, bad, hope good, actually, want good
Topic 6: wait, fuck wait, wait come, love wait, hear, come, release, rob, wait hear, thank
Topic 7: awesome, look awesome, check, pretty awesome, thanks, pretty, damn, gon, thank, lego
Topic 8: really, want, enjoy, really enjoy, like, really want, hope, really like, really good, really really

=== 2015 ===
Movie Year Range: 2014-2015
Median Movie Year: 2015
Top Movie Periods:
  - 2013-2017: 12777 comments
  - 2012-2016: 3883 comments

Topics:
Topic 1: movies, make, people, think, great, time, want, better, come, bad
Topic 2: look, look forward, forward, look great, look good, great, poster, trailer look, really look, look amazing
Topic 3: good, look good, pretty good, really good, pretty, dinosaur, hope good, good dinosaur, hope, good good
Topic 4: trailer, trailer look, watch trailer, saw trailer, saw, song, pretty, trailer make, spoil, soon
Topic 5: watch, want, ill, want watch, ill watch, worth, worth watch, watch trailer, time, night
Topic 6: really, like, enjoy, really enjoy, really like, really good, hope, really want, actually, really look
Topic 7: love, man, really love, fuck, saw, great, fuck love, love movies, definitely, glad
Topic 8: wait, fuck, star_wars wars, star_wars, wars, magical, genuinely, ill wait, come, ill

=== 2016 ===
Movie Year Range: 2014-2016
Median Movie Year: 2016
Top Movie Periods:
  - 2014-2018: 14733 comments
  - 2013-2017: 3299 comments
  - 2012-2016: 1859 comments

Topics:
Topic 1: movies, make, people, want, come, think, time, end, bad, better
Topic 2: look, look forward, forward, look good, look pretty, awesome, look great, fun, look really, poster
Topic 3: good, pretty, pretty good, look good, really good, good good, good job, hope good, job, good time
Topic 4: trailer, song, saw, saw trailer, trailer make, trailer look, watch trailer, trailers, excited, music
Topic 5: love, wait, really love, love love, absolutely, god, man, love movies, films, absolutely love
Topic 6: really, like, enjoy, really enjoy, really good, really like, hope, really want, really hope, really really
Topic 7: watch, ill, ill watch, night, enjoy, time, video, watch trailer, worth watch, netflix
Topic 8: great, look great, think, performance, cast, think great, arrival, great cast, fun, really great

=== 2017 ===
Movie Year Range: 2014-2017
Median Movie Year: 2017
Top Movie Periods:
  - 2015-2019: 15936 comments
  - 2014-2018: 4488 comments
  - 2013-2017: 1615 comments

Topics:
Topic 1: make, movies, people, come, time, want, think, way, end, best
Topic 2: look, look forward, forward, look good, look great, look pretty, awesome, wait, fun, look terrible
Topic 3: good, really good, pretty, pretty good, look good, good good, bad, hope good, reviews, actually
Topic 4: love, absolutely, absolutely love, really love, fuck love, love love, guys, hate, meh, work
Topic 5: trailer, watch trailer, song, saw, saw trailer, use, trailer look, excited, trailers, release
Topic 6: fuck, shit, holy, fuck love, wait, fucking, man, holy shit, holy fuck, thread
Topic 7: really, great, enjoy, really enjoy, like, really good, think, really like, really really, really great
Topic 8: watch, ill, watch trailer, ill watch, want watch, want, fun, time, times, night

=== 2018 ===
Movie Year Range: 2014-2018
Median Movie Year: 2018
Top Movie Periods:
  - 2016-2020: 16786 comments
  - 2015-2019: 4322 comments
  - 2013-2017: 1667 comments

Topics:
Topic 1: make, movies, time, people, great, think, come, want, best, end
Topic 2: look, forward, look forward, poster, look great, fun, great, look good, look pretty, amazing
Topic 3: good, pretty, really good, pretty good, bad, hope, look good, damn, good time, good good
Topic 4: love, favorite, absolutely, absolutely love, simon, excited, dogs, fuck love, saw, love love
Topic 5: really, enjoy, really enjoy, like, really like, really good, hope, really really, really hope, excited
Topic 6: trailer, saw trailer, saw, watch trailer, trailer look, excited, release, teaser, better, trailer make
Topic 7: watch, ill, movies, fuck, ill watch, worth watch, worth, definitely, fun, night
Topic 8: wait, wait watch, meg, come, fucking, months, theater, release, years, infinity war

=== 2019 ===
Movie Year Range: 2014-2019
Median Movie Year: 2019
Top Movie Periods:
  - 2017-2021: 14593 comments
  - 2016-2020: 3519 comments
  - 2014-2018: 1755 comments

Topics:
Topic 1: watch, movies, make, time, great, people, want, come, think, best
Topic 2: look, look forward, forward, poster, look good, look great, great, fun, look terrible, look fun
Topic 3: good, pretty, pretty good, really good, look good, good good, bad, guess, good thing, feel good
Topic 4: love, absolutely, absolutely love, love love, witch, story, really love, fuck love, fucking, spielberg
Topic 5: wait, years, come, release, wait watch, wait years, hear, eyes, reboot, gon
Topic 6: trailer, saw, saw trailer, better, watch trailer, trailer look, dogs, song, spoil, release
Topic 7: fuck, shit, gon, man, ready, fuck love, holy, say, fucking, hereditary
Topic 8: really, enjoy, really enjoy, like, hope, really good, really like, really hope, really want, really really

=== 2020 ===
Movie Year Range: 2014-2020
Median Movie Year: 2018
Top Movie Periods:
  - 2018-2022: 4049 comments
  - 2017-2021: 3767 comments
  - 2015-2019: 2044 comments

Topics:
Topic 1: really, movies, time, make, people, come, enjoy, think, look, end
Topic 2: watch, ill, ill watch, netflix, fun, watch home, want watch, watch trailer, times, home
Topic 3: good, really good, really, pretty, pretty good, bad, horror, job, good thing, good job
Topic 4: busan, train busan, train, peninsula, sequel, zombie, zombies, netflix, present, korean
Topic 5: love, absolutely love, absolutely, love movies, movies, really love, fuck love, fuck, love love, kid
Topic 6: great, look great, cast, really great, job, great job, fantastic, films, great watch, start
Topic 7: release, theaters, want, tenet, date, release date, new, money, stream, new mutants
Topic 8: wait, fuck, finally, wait watch, years, exciting, new, favorite, news, week

=== 2021 ===
Movie Year Range: 2014-2021
Median Movie Year: 2020
Top Movie Periods:
  - 2019-2023: 8068 comments
  - 2013-2017: 1904 comments
  - 2014-2018: 1569 comments

Topics:
Topic 1: movies, time, make, people, end, want, come, think, bad, scene
Topic 2: watch, ill, ill watch, definitely, absolutely, fun watch, watch time, night, enjoy, time watch
Topic 3: good, really good, pretty, pretty good, good good, good thing, look good, enjoyable, hope good, good guy
Topic 4: love, absolutely, absolutely love, favorite, love love, really love, soundtrack, monsters, song, wait
Topic 5: look, poster, look forward, forward, trailer, fun, look fun, cool, look great, poster look
Topic 6: great, cast, look great, story, best, think, soundtrack, fantastic, actor, check
Topic 7: really, enjoy, like, really enjoy, really like, really good, really really, hope, fun, really hope
Topic 8: fuck, lol, man, wait, holy, holy fuck, actual, shit, fucking, fuck love

=== 2022 ===
Movie Year Range: 2014-2022
Median Movie Year: 2021
Top Movie Periods:
  - 2020-2024: 8952 comments
  - 2019-2023: 2131 comments
  - 2013-2017: 1876 comments

Topics:
Topic 1: really, make, people, think, way, want, come, feel, story, better
Topic 2: love, absolutely, absolutely love, love love, kid, wait, really love, hate, amazing, favorite
Topic 3: good, really good, pretty, pretty good, look good, really, good time, good way, good good, think good
Topic 4: watch, ill, want watch, ill watch, watch trailer, trailer, want, times, fun, definitely
Topic 5: look, look forward, forward, trailer, fun, poster, look good, look fun, look pretty, look great
Topic 6: movies, favorite, best, year, action, marvel_cinematic_universe, cinematic_universe, action movies, movies year, favorite movies
Topic 7: time, long, long time, favorite, man, good time, best, waste, time watch, watch time
Topic 8: great, think, think great, man, cast, look great, hell, soundtrack, fantastic, michael

=== 2023 ===
Movie Year Range: 2014-2023
Median Movie Year: 2021
Top Movie Periods:
  - 2021-2025: 7916 comments
  - 2020-2024: 3079 comments
  - 2013-2017: 1999 comments

Topics:
Topic 1: movies, really, make, people, great, time, feel, want, think, end
Topic 2: love, absolutely love, absolutely, love love, hate, story, love story, man, really love, music
Topic 3: good, really good, really, pretty good, pretty, bad, look good, hope, hope good, think good
Topic 4: look, look forward, forward, fun, trailer, look good, computer_generated_imagery, look fun, look great, great
Topic 5: watch, time, want watch, time watch, want, ill, times, watch time, ill watch, watch watch
Topic 6: wick, john, john wick, bourne, kill, long, jason, action, jason bourne, win
Topic 7: poster, poster look, gorgeous, posters, awful, cool, official, cool poster, nice, tagline
Topic 8: wait, fuck, wait watch, release, fuck wait, years, book, dead, love wait, black

=== 2024 ===
Movie Year Range: 2014-2024
Median Movie Year: 2019
Top Movie Periods:
  - 2022-2026: 4181 comments
  - 2013-2017: 3226 comments
  - 2017-2021: 3039 comments

Topics:
Topic 1: make, people, come, feel, want, way, work, end, think, need
Topic 2: love, favorite, absolutely love, absolutely, really love, hat, love love, fuck love, godzilla, love story
Topic 3: watch, ill, times, ill watch, fun, want watch, watch time, night, fun watch, watch movies
Topic 4: good, really good, pretty good, pretty, bad, poster, good good, look good, good time, excited
Topic 5: look, look forward, forward, poster, sound, trailer, godzilla, fun, look good, look fun
Topic 6: time, long, long time, watch time, favorite, hear, good time, time watch, travel, saw
Topic 7: really, great, enjoy, like, really enjoy, really like, fun, really good, lot, story
Topic 8: movies, best, favorite, godzilla, favorite movies, watch movies, horror, action, best movies, movies watch

Saving yearly topic data...

=== Enhanced Temporal Analysis Complete ===
Output files:
- yearly_analysis/yearly_topic_report.txt (detailed topics by year)
- yearly_analysis/ (CSVs of document-topic distributions)
- yearly_analysis/yearly_topic_counts.png (visualization)
- yearly_analysis/period_coverage_heatmap.png (movie period coverage)

2.5 Topic Modeling Methodology_LDA

import pandas as pd

comment_data_LDA = comment_data_final.copy()

comment_data_LDA.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 222520 entries, 0 to 222519
Data columns (total 14 columns):
 #   Column          Non-Null Count   Dtype  
---  ------          --------------   -----  
 0   Movie Title     222520 non-null  object 
 1   Year            222520 non-null  int64  
 2   Post Title      222520 non-null  object 
 3   Post Author     222520 non-null  object 
 4   Post Time       222520 non-null  object 
 5   Post Score      222520 non-null  float64
 6   Comment         222520 non-null  object 
 7   Comment Author  222520 non-null  object 
 8   Comment Time    222520 non-null  object 
 9   Comment Score   222520 non-null  float64
 10  Comment Year    222520 non-null  int64  
 11  processed_text  222520 non-null  object 
 12  comment_year    222520 non-null  int32  
 13  movie_period    222520 non-null  object 
dtypes: float64(2), int32(1), int64(2), object(9)
memory usage: 22.9+ MB

comment_data_LDA.to_csv('comment_data_LDA_three.csv', index=False, quoting=1) 

LDA_data = pd.read_csv('comment_data_LDA_three.csv', skip_blank_lines=True)

LDA_data

	Movie Title	Year	Post Title	Post Author	Post Time	Post Score	Comment	Comment Author	Comment Time	Comment Score	Comment Year	processed_text	comment_year	movie_period
0	La La Land	2016	Just re-watched La La Land	Natergator97	2023-07-21 16:54:17	1786.0	I dont understand people who have a gripe with...	--------rook	2023-07-22 01:08:52	8.0	2023	understand people gripe ending favorite part p...	2023	2014-2018
1	Allegiant	2016	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	98.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016	fuck ill jeff daniels fucking bad scene end bl...	2016	2014-2018
2	Divergent	2014	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	104.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016	fuck ill jeff daniels fucking bad scene end bl...	2016	2012-2016
3	Soul	2020	Pixar’s ‘Soul’ Release Delayed Until November	BunyipPouch	2020-04-13 18:04:30	2431.0	I really dont watch big block busters but seei...	-----godspeed-----	2020-04-13 20:06:58	9.0	2020	really watch big block busters see many movies...	2020	2018-2022
4	The Lego Movie	2014	In The Lego Movie, when master builders move q...	notamustache	2014-06-11 12:52:39	3144.0	And so are the lens flares.	----0----	2014-06-11 16:26:09	39.0	2014	lens flares	2014	2012-2016
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
222515	Terminator: Dark Fate	2019	'Terminator 2: Judgment Day' fan screening tur...	zzman1490	2019-10-21 02:48:59	47447.0	Just got back from one of the screenings, inte...	zzman1490	2019-10-21 02:52:32	5781.0	2019	get back screenings interesting idea marketing...	2019	2017-2021
222516	Spider-Man: Into the Spider-Verse	2018	'Spider-Man: Into the Spider-Verse' Review Meg...	Sisiwakanamaru	2018-11-28 13:26:52	1765.0	Man, I expected this movie to be good, and I w...	zzuxon	2018-11-28 17:24:52	76.0	2018	man expect good really excited expect fantasti...	2018	2016-2020
222517	Black Adam	2022	BLACK ADAM - Official Trailer 2	Turbostrider27	2022-09-08 23:45:49	440.0	This looks like dawn of the seven from the boys	zzz099	2022-09-09 15:06:12	3.0	2022	look dawn boys	2022	2020-2024
222518	Amy	2015	First Images of Amy Adams in 'Nightbitch' - A ...	MarvelsGrantMan136	2024-07-18 15:09:10	2398.0	I really hope this is one of those movies that...	zzzzarf	2024-07-18 23:54:39	12.0	2024	really hope movies tense unsparing portrait ps...	2024	2013-2017
222519	Elemental	2023	Official Discussion: Elemental [SPOILERS]	mi-16evil	2023-06-16 02:02:55	657.0	This movie made me sob. As the eldest daughter...	zzzzooooiiiiinnnkkkk	2023-10-03 14:08:12	81.0	2023	make sob eldest daughter immigrants saw ember ...	2023	2021-2025

222520 rows × 14 columns

# create comment_id
LDA_data['comment_id'] = range(1, len(LDA_data) + 1)

# outcome
print(LDA_data[['comment_id', 'Comment']].head())

   comment_id                                            Comment
0           1  I dont understand people who have a gripe with...
1           2  Fuck it Ill just say it, Jeff Daniels was so f...
2           3  Fuck it Ill just say it, Jeff Daniels was so f...
3           4  I really dont watch big block busters but seei...
4           5                        And so are the lens flares.

LDA_data

	Movie Title	Year	Post Title	Post Author	Post Time	Post Score	Comment	Comment Author	Comment Time	Comment Score	Comment Year	processed_text	comment_year	movie_period	comment_id
0	La La Land	2016	Just re-watched La La Land	Natergator97	2023-07-21 16:54:17	1786.0	I dont understand people who have a gripe with...	--------rook	2023-07-22 01:08:52	8.0	2023	understand people gripe ending favorite part p...	2023	2014-2018	1
1	Allegiant	2016	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	98.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016	fuck ill jeff daniels fucking bad scene end bl...	2016	2014-2018	2
2	Divergent	2014	Official Discussion - The Divergent Series: Al...	r_antrobus	2016-03-19 03:03:17	104.0	Fuck it Ill just say it, Jeff Daniels was so f...	------Username------	2016-03-19 04:45:48	171.0	2016	fuck ill jeff daniels fucking bad scene end bl...	2016	2012-2016	3
3	Soul	2020	Pixar’s ‘Soul’ Release Delayed Until November	BunyipPouch	2020-04-13 18:04:30	2431.0	I really dont watch big block busters but seei...	-----godspeed-----	2020-04-13 20:06:58	9.0	2020	really watch big block busters see many movies...	2020	2018-2022	4
4	The Lego Movie	2014	In The Lego Movie, when master builders move q...	notamustache	2014-06-11 12:52:39	3144.0	And so are the lens flares.	----0----	2014-06-11 16:26:09	39.0	2014	lens flares	2014	2012-2016	5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
222515	Terminator: Dark Fate	2019	'Terminator 2: Judgment Day' fan screening tur...	zzman1490	2019-10-21 02:48:59	47447.0	Just got back from one of the screenings, inte...	zzman1490	2019-10-21 02:52:32	5781.0	2019	get back screenings interesting idea marketing...	2019	2017-2021	222516
222516	Spider-Man: Into the Spider-Verse	2018	'Spider-Man: Into the Spider-Verse' Review Meg...	Sisiwakanamaru	2018-11-28 13:26:52	1765.0	Man, I expected this movie to be good, and I w...	zzuxon	2018-11-28 17:24:52	76.0	2018	man expect good really excited expect fantasti...	2018	2016-2020	222517
222517	Black Adam	2022	BLACK ADAM - Official Trailer 2	Turbostrider27	2022-09-08 23:45:49	440.0	This looks like dawn of the seven from the boys	zzz099	2022-09-09 15:06:12	3.0	2022	look dawn boys	2022	2020-2024	222518
222518	Amy	2015	First Images of Amy Adams in 'Nightbitch' - A ...	MarvelsGrantMan136	2024-07-18 15:09:10	2398.0	I really hope this is one of those movies that...	zzzzarf	2024-07-18 23:54:39	12.0	2024	really hope movies tense unsparing portrait ps...	2024	2013-2017	222519
222519	Elemental	2023	Official Discussion: Elemental [SPOILERS]	mi-16evil	2023-06-16 02:02:55	657.0	This movie made me sob. As the eldest daughter...	zzzzooooiiiiinnnkkkk	2023-10-03 14:08:12	81.0	2023	make sob eldest daughter immigrants saw ember ...	2023	2021-2025	222520

222520 rows × 15 columns

LDA_data.to_csv('original_data.csv', index=False, quoting=1) 

import pandas as pd
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
from gensim.models import LdaModel
from gensim.corpora import Dictionary
from gensim.models.coherencemodel import CoherenceModel
import pyLDAvis.gensim_models
import os

print(LDA_data['processed_text'].isnull().sum())

1336

LDA_data['processed_text'] = LDA_data['processed_text'].fillna('')

LDA_data['processed_text'] = LDA_data['processed_text'].astype(str)

import pandas as pd
import numpy as np
from gensim.corpora import Dictionary
from gensim.models import LdaModel, CoherenceModel
import pyLDAvis.gensim_models
import os
import matplotlib.pyplot as plt
from tqdm import tqdm

# 1. Prepare data (assuming data is already aligned by year)
def prepare_annual_data(comment_data):
    """
    Prepare textual data by year
    Returns: {year: [tokenized_docs]}
    """
    annual_data = {}
    for year, group in comment_data.groupby('comment_year'):
        # Convert processed_text column strings to lists of words
        annual_data[year] = group['processed_text'].apply(lambda x: x.split()).tolist()  
    return annual_data

# 2. LDA Model Trainer
class AnnualLDATrainer:
    def __init__(self, min_topics=5, max_topics=10, random_state=42):
        self.min_topics = min_topics
        self.max_topics = max_topics
        self.random_state = random_state
        self.models = {}  # Store models for each year
        self.coherence_scores = {}  # Store coherence scores for each year
        self.optimal_topics = {}  # Store optimal topic numbers for each year
        
    def _compute_coherence(self, model, texts, dictionary):
        """Calculate topic coherence score"""
        coherence = CoherenceModel(
            model=model,
            texts=texts,
            dictionary=dictionary,
            coherence='c_v'
        )
        return coherence.get_coherence()
    
    def find_optimal_topics(self, annual_data):
        """Find optimal topic number for each year"""
        print("\n=== Finding Optimal Topics ===")
        
        for year, docs in tqdm(annual_data.items(), desc="Year Progress"):
            # Create dictionary and corpus
            dictionary = Dictionary(docs)
            corpus = [dictionary.doc2bow(doc) for doc in docs]
            
            # Grid search for optimal topic number
            coherence_values = []
            for n_topics in range(self.min_topics, self.max_topics + 1):
                model = LdaModel(
                    corpus=corpus,
                    id2word=dictionary,
                    num_topics=n_topics,
                    random_state=self.random_state,
                    passes=10,
                    alpha='auto'
                )
                coherence = self._compute_coherence(model, docs, dictionary)
                coherence_values.append(coherence)
            
            # Select optimal topic number
            optimal_idx = np.argmax(coherence_values)
            optimal_n = range(self.min_topics, self.max_topics + 1)[optimal_idx]
            self.optimal_topics[year] = optimal_n
            
            # Store best model
            best_model = LdaModel(
                corpus=corpus,
                id2word=dictionary,
                num_topics=optimal_n,
                random_state=self.random_state,
                passes=15,
                alpha='auto'
            )
            self.models[year] = best_model
            self.coherence_scores[year] = coherence_values[optimal_idx]
            
        return self.models
    
    def assign_topic_distributions(self, comment_data):
        """Assign topic probabilities to each comment (using original comment_id)"""
        print("\nAssigning topic distributions...")
        results = []
        
        for year, model in tqdm(self.models.items(), desc="Processing Years"):
            # Get all data for this year (preserve original comment_id)
            year_data = comment_data[comment_data['comment_year'] == year]
            
            # Create dictionary for that year
            dictionary = Dictionary(year_data['processed_text'].apply(lambda x: x.split()))
            
            # Process each document with its original comment_id
            for _, row in year_data.iterrows():
                bow = dictionary.doc2bow(row['processed_text'].split())
                topic_dist = model.get_document_topics(bow)
                dominant_topic = max(topic_dist, key=lambda x: x[1])[0] if topic_dist else -1
                
                results.append({
                    'comment_id': row['comment_id'],  # Using original comment_id
                    'year': year,
                    'topic_distribution': dict(topic_dist),
                    'dominant_topic': dominant_topic
                })
        
        return pd.DataFrame(results)
    
    def generate_topic_report(self, n_words=10):
        """Generate topic keywords report"""
        report = []
        for year in sorted(self.models.keys()):
            model = self.models[year]
            report.append(f"\n=== {year} (Optimal Topics: {self.optimal_topics[year]}, Coherence: {self.coherence_scores[year]:.3f}) ===")
            
            for topic_id in range(model.num_topics):
                words = model.show_topic(topic_id, topn=n_words)
                report.append(f"Topic {topic_id + 1}: {', '.join([w[0] for w in words])}")
        
        return '\n'.join(report)
    
    def visualize_topics(self, comment_data, output_dir="lda_visualization"):
        """Generate interactive visualizations"""
        os.makedirs(output_dir, exist_ok=True)
        for year, model in self.models.items():
            year_data = comment_data[comment_data['comment_year'] == year]
            dictionary = Dictionary(year_data['processed_text'].apply(lambda x: x.split()))
            corpus = [dictionary.doc2bow(doc.split()) for doc in year_data['processed_text']]
            
            vis = pyLDAvis.gensim_models.prepare(model, corpus, dictionary)
            pyLDAvis.save_html(vis, f"{output_dir}/lda_{year}.html")

# 3. Execute analysis
if __name__ == "__main__":
    print("Preparing annual data...")
    annual_data = prepare_annual_data(LDA_data)

    
    # Train models
    print("\nTraining LDA models...")
    trainer = AnnualLDATrainer(min_topics=5, max_topics=10)
    models = trainer.find_optimal_topics(annual_data)
    
    # Generate outputs
    print("\nGenerating outputs...")
    topic_report = trainer.generate_topic_report()
    print(topic_report)
    
    # Save results
    output_dir = "topic_modeling_results"
    os.makedirs(output_dir, exist_ok=True)
    
    # 1. Save topic report
    with open(f"{output_dir}/annual_topic_report.txt", "w") as f:
        f.write(topic_report)
    
    # 2. Save topic distributions (now with correct comment_id matching)
    topic_dist_df = trainer.assign_topic_distributions(LDA_data)
    topic_dist_df.to_csv(f"{output_dir}/document_topic_distributions.csv", index=False)
    
    # 3. Visualization
    trainer.visualize_topics(LDA_data, output_dir)
    
    # 4. Save optimal topic numbers by year
    pd.DataFrame.from_dict(trainer.optimal_topics, orient='index').to_csv(
        f"{output_dir}/optimal_topics_per_year.csv", header=["optimal_topics"])
    
    # 5. Plot optimal topic number selection
    plt.figure(figsize=(10, 6))
    years = sorted(trainer.optimal_topics.keys())
    plt.bar(years, [trainer.optimal_topics[y] for y in years])
    plt.title("Optimal Number of Topics per Year")
    plt.xlabel("Year")
    plt.ylabel("Topic Count")
    plt.xticks(years, rotation=45)
    plt.tight_layout()
    plt.savefig(f"{output_dir}/optimal_topics_plot.png")
    
    print("\n=== Topic Modeling Complete ===")
    print(f"Results saved to: {output_dir}/")
    print("- annual_topic_report.txt (detailed topics by year)")
    print("- document_topic_distributions.csv (per-document topic assignments with correct comment_id)")
    print("- lda_visualization/ (interactive topic visualizations)")
    print("- optimal_topics_plot.png (visualization of topic counts)")

Preparing annual data...

Training LDA models...

=== Finding Optimal Topics ===

Year Progress: 100%|████████████████████████████| 11/11 [18:08<00:00, 98.93s/it]

Generating outputs...

=== 2014 (Optimal Topics: 10, Coherence: 0.526) ===
Topic 1: 3d_technology, respect, win, imax, knowledge, preview, special_effects, stephen, nancy, lucy
Topic 2: apes, street, jump, kids, planet, james, rise, tim, tatum, quality
Topic 3: really, look, good, go, love, great, watch, get, much, make
Topic 4: action, ever, kill, people, scene, fight, die, use, time, make
Topic 5: sony, transformers, mark, spiderman, box, johnny, release, cry, office, optimus
Topic 6: god, earth, date, horror, woman, light, noah, wife, giver, suck
Topic 7: release, city, song, green, article, review, edge, heres, eva, sin
Topic 8: fuck, chris, michael, north, seth, tv, korea, design, film, hype
Topic 9: cruise, dumb, tom, enigma, accent, bill, turing, clip, code, moses
Topic 10: poster, joke, disney, jesus, maleficent, jennifer, thomas, us, armor, sign

=== 2015 (Optimal Topics: 9, Coherence: 0.538) ===
Topic 1: max, tom, woman, mad, fury, road, holy, rock, reddit, theatre
Topic 2: poster, video, drive, game, sniper, american, car, games, parents, accent
Topic 3: oscar, win, jobs, steve, michael, list, double, fassbender, nominate, award
Topic 4: golden, martian, fang, answer, capture, us, date, questions, link, summer
Topic 5: animate, god, stunt, posters, jupiter, gross, vin, diesel, draw, interstellar
Topic 6: horror, depp, jump, del, scary, cinematography, humor, johnny, documentary, tomorrow
Topic 7: bond, gon, james, sandler, song, adam, marketing, shitty, history, single
Topic 8: 3d_technology, star_wars, wars, warrior, name, imax, warlord, screening, anyway, land
Topic 9: really, good, go, watch, get, movies, look, make, people, love

=== 2016 (Optimal Topics: 6, Coherence: 0.564) ===
Topic 1: win, holy, chris, lee, pratt, smith, oscar, breathe, ability, sing
Topic 2: mil, weekend, bourne, worldwide, domestic, gross, shark, superman, jason, captain
Topic 3: max, chinese, gods, posters, typical, hunt, daniel, dollars, fury, screens
Topic 4: giant, bfg, army, mel, slightly, dr, wtf, youtube, accent, swiss
Topic 5: movies, films, come, look, still, best, people, wait, years, hope
Topic 6: really, good, love, go, make, watch, great, time, much, get

=== 2017 (Optimal Topics: 6, Coherence: 0.537) ===
Topic 1: watch, love, go, get, end, time, scene, want, only, say
Topic 2: fucking, jackie, mark, holy, lol, mil, ass, chan, hot, large
Topic 3: really, good, movies, make, look, great, people, much, very, see
Topic 4: john, marvel_cinematic_universe, cinematic_universe, wick, women, deadpool, filmmakers, insane, valerian, franco
Topic 5: best, oscar, win, water, land, picture, lady, support, tom, bird
Topic 6: release, budget, weekend, worldwide, week, star_wars, logan, gross, baby, domestic

=== 2018 (Optimal Topics: 6, Coherence: 0.528) ===
Topic 1: character, story, book, read, life, characters, write, family, base, day
Topic 2: release, weekend, week, films, budget, box, domestic, open, office, theaters
Topic 3: john, dog, ready, universe, curious, tomb, favourite, raider, king, mouth
Topic 4: star_wars, wars, mark, x200b, chinese, rogers, nun, wear, blue, jedi
Topic 5: black, worldwide, panther, war, gross, marvel_cinematic_universe, pass, jurassic, greatest, cinematic_universe
Topic 6: really, good, make, love, go, get, movies, watch, time, look

=== 2019 (Optimal Topics: 6, Coherence: 0.494) ===
Topic 1: budget, marvel_cinematic_universe, room, fine, cinematic_universe, totally, evil, toy, half, cats
Topic 2: really, good, go, make, watch, love, get, movies, look, time
Topic 3: godzilla, sleep, positive, maleficent, doctor, boys, robert, joe, perhaps, de
Topic 4: john, tom, article, wick, dogs, gross, worldwide, dog, jojo, elton
Topic 5: ad, cat, awards, award, astra, count, sex, domestic, choice, gorgeous
Topic 6: die, poster, kill, place, dead, space, rock, suck, rhapsody, escape

=== 2020 (Optimal Topics: 10, Coherence: 0.558) ===
Topic 1: train, parasite, win, busan, deserve, zombie, oscar, korean, driver, YEAR_2020
Topic 2: jojo, ford, rush, rabbit, tom, return, reddit, jones, wanna, joker
Topic 3: mutants, feeling, stream, ruin, hot, jack, cinematic, silly, report, suicide
Topic 4: trolls, pop, boys, eat, scoobydoo, witch, midsommar, sign, sub, scar
Topic 5: directors, class, handmaiden, fly, feature, invisible, company, credit, suit, feelings
Topic 6: john, role, excellent, box, office, star, worse, mystery, involve, history
Topic 7: really, good, go, watch, make, movies, time, love, get, people
Topic 8: king, blue, joke, cat, wild, america, agent, scooby, husband, david
Topic 9: marvel_cinematic_universe, cats, cinematic_universe, art, captain, crowe, documentary, russell, iron, business
Topic 10: great, watch, best, read, wait, favorite, amazing, book, kid, netflix

=== 2021 (Optimal Topics: 10, Coherence: 0.531) ===
Topic 1: ultimately, title, gravity, suspicion, twist, arm, ryan, recall, develop, peoples
Topic 2: trucks, ensemble, cheer, ready, ritchie, lord_of_the_rings, project, hole, heat, halloween
Topic 3: strange, group, final, tom, send, news, sleep, message, fake, mr
Topic 4: kill, space, spin, truck, boat, mission, cruella, body, case, blind
Topic 5: disney, john, suit, bond, michael, island, failure, previously, company, taskmaster
Topic 6: watch, movies, love, look, good, see, still, years, really, trailer
Topic 7: godzilla, chance, kong, king, die, evangelion, write, star, giant, driver
Topic 8: rock, money, different, heist, also, difficult, risk, memories, production, spiral
Topic 9: really, make, go, get, people, take, time, only, also, great
Topic 10: spiderman, marvel_cinematic_universe, cinematic_universe, black, free, build, white, YEAR_2021, iron, matrix

=== 2022 (Optimal Topics: 8, Coherence: 0.585) ===
Topic 1: poster, michael, friend, george, mirabel, christmas, minions, listen, bay, wear
Topic 2: marvel_cinematic_universe, cinematic_universe, office, box, rock, universe, batman, likely, adam, eye
Topic 3: strange, star, fast, dr, YEAR_2022, suicide, kingdom, squad, posters, dwayne
Topic 4: try, people, end, fuck, use, take, family, life, point, kill
Topic 5: imax, worse, months, animate, avatar, 3d_technology, okay, odd, musical, thor
Topic 6: james, human, sonic, bruno, onion, art, wild, lee, chris, door
Topic 7: history, elvis, picture, light, oscar, king, win, feature, choose, ignore
Topic 8: really, watch, good, movies, love, make, get, go, time, great

=== 2023 (Optimal Topics: 9, Coherence: 0.587) ===
Topic 1: black, school, roles, cry, soundtrack, complete, jack, week, appear, multiple
Topic 2: napoleon, bourne, star_wars, jason, beat, wars, beast, roll, film, sub
Topic 3: marvel_cinematic_universe, cinematic_universe, recommend, paul, review, thanks, rudd, creed, highly, hot
Topic 4: imax, certain, fly, bigger, credits, audiences, glass, split, planet, dog
Topic 5: john, wick, wes, anderson, marketing, holy, muppets, plan, reboot, mission
Topic 6: voice, tom, david, office, dc_extended_universe, box, opening, max, cruise, lmao
Topic 7: go, people, get, want, even, take, way, end, only, make
Topic 8: look, wait, book, poster, godzilla, forward, michael, video, mario, oppenheimer
Topic 9: watch, really, good, movies, love, great, see, much, very, feel

=== 2024 (Optimal Topics: 7, Coherence: 0.574) ===
Topic 1: effects, computer_generated_imagery, team, robert, david, cars, type, gun, visual_effects, oppenheimer
Topic 2: watch, movies, go, make, time, get, want, see, come, work
Topic 3: music, john, game, story, shoot, script, base, mention, play, world
Topic 4: themes, burton, jason, statham, anxiety, robot, mandalorian, fiction, tim, culture
Topic 5: love, really, good, great, much, people, look, feel, very, too
Topic 6: king, train, ryan, sell, road, lion, pay, joy, market, gosling
Topic 7: godzilla, poster, star_wars, mr, dune, shin, max, ass, amy, smith

Assigning topic distributions...

Processing Years: 100%|█████████████████████████| 11/11 [00:14<00:00,  1.29s/it]

=== Topic Modeling Complete ===
Results saved to: topic_modeling_results/
- annual_topic_report.txt (detailed topics by year)
- document_topic_distributions.csv (per-document topic assignments with correct comment_id)
- lda_visualization/ (interactive topic visualizations)
- optimal_topics_plot.png (visualization of topic counts)

2.6 Comment Data Analysis

2.6.1 Key Topics in Word Cloud Analysis

from wordcloud import WordCloud
import matplotlib.pyplot as plt
import os

def generate_annual_wordclouds(trainer, output_dir="wordclouds", n_words=20):
    """
    Generate a word cloud for keywords of all topics for each year.
    
    Parameters:
        trainer: An instance of the trained AnnualLDATrainer.
        output_dir: Output directory for the word clouds.
        n_words: Number of top keywords to extract from each topic.
    """
    os.makedirs(output_dir, exist_ok=True)
    
    for year, model in trainer.models.items():
        # Combine all topic keywords for the year
        all_keywords = []
        for topic_id in range(model.num_topics):
            words = model.show_topic(topic_id, topn=n_words)
            all_keywords.extend([w[0] for w in words])
        
        # Generate the word cloud
        text = ' '.join(all_keywords)
        wordcloud = WordCloud(
            width=800, 
            height=400,
            background_color='white',
            colormap='viridis',
            max_words=100
        ).generate(text)
        
        # Save and display the word cloud
        plt.figure(figsize=(10, 5))
        plt.imshow(wordcloud, interpolation='bilinear')
        plt.axis('off')
        plt.title(f'Movie Discussion Topics - {year}', fontsize=15)
        plt.savefig(f"{output_dir}/wordcloud_{year}.png", bbox_inches='tight', dpi=300)
        plt.show()  # Display the word cloud
        plt.close()
    
    print(f"Word clouds have been saved to: {output_dir}/")

# Example usage (to be added after your main code)
if __name__ == "__main__":
    # Assume you have run your code and obtained the trainer instance
    trainer = AnnualLDATrainer()
    models = trainer.find_optimal_topics(annual_data)
    
    # Generate word clouds
    generate_annual_wordclouds(trainer)
    
    print("=== Annual word clouds generation completed ===")

=== Finding Optimal Topics ===

Year Progress: 100%|████████████████████████████| 11/11 [17:40<00:00, 96.44s/it]

Word clouds have been saved to: wordclouds/
=== Annual word clouds generation completed ===

2.6.2 Theme Evolution Analysis (Based on the original labeled dataset)(Abandoned)

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
from textwrap import wrap
import squarify
from scipy import stats
import seaborn as sns
import re

# Read dataset
df = pd.read_csv('/Users/heshuqing0305/Downloads/movie_topics_labeled.csv')

# Check missing value
print(df.isnull().sum())

# Number of topics by year
topic_counts = df.groupby('Year')['Topic#'].count()
print(topic_counts)

Year               0
Topic#             0
Top 3 Keywords     0
Label              0
Category           0
Confidence         0
Unnamed: 6        87
Unnamed: 7        87
Unnamed: 8        87
Unnamed: 9        87
Unnamed: 10       87
Unnamed: 11       87
Unnamed: 12       87
Unnamed: 13       87
Unnamed: 14       87
Unnamed: 15       87
Unnamed: 16       87
Unnamed: 17       87
Unnamed: 18       87
Unnamed: 19       87
dtype: int64
Year
2014    10
2015     9
2016     6
2017     6
2018     6
2019     6
2020    10
2021    10
2022     8
2023     9
2024     7
Name: Topic#, dtype: int64

df = df.drop(columns=df.columns[6:20])  # Delete useless data

print(df.duplicated(subset=['Year', 'Topic#']).sum())

df

0

	Year	Topic#	Top 3 Keywords	Label	Category	Confidence
0	2014	1	3d_technology, imax	IMAX/3D Technology Discussion	Technology	5
1	2014	2	apes, planet, rise	Planet of the Apes Franchise	Franchise	4
2	2014	3	really, good, love	Generic Positive Sentiment	Reception	1
3	2014	4	action, kill, fight	Action/Violence Themes	Genre	4
4	2014	5	sony, transformers	Transformers Box Office	Industry	5
...	...	...	...	...	...	...
82	2024	3	music, game, script	Music in Video Game Movies	Production	3
83	2024	4	themes, anxiety, robot	AI/Robot Themes in Film	Genre	3
84	2024	5	love, really, good	Generic Positive Sentiment	Reception	1
85	2024	6	king, train, gosling	The Fall Guy/Ryan Gosling	Talent	3
86	2024	7	godzilla, dune	Godzilla/Dune Cross-talk	Franchise	4

87 rows × 6 columns

df.to_csv('topic_data.csv', index=False, quoting=1)

df.groupby('Year')['Topic#'].count().plot(kind='bar', title='Topics per Year')
plt.xlabel('Year')
plt.ylabel('Topic Count')

Text(0, 0.5, 'Topic Count')

original_dist = df['Category'].value_counts(normalize=True).mul(100).round(1)
print("Original category distribution:\n", original_dist)

# Plot category proportion treemap (requires squarify)
plt.figure(figsize=(10, 6))
squarify.plot(
    sizes=original_dist.values,
    label=[f"{k}\n{v}%" for k, v in original_dist.items()],
    alpha=0.8
)
plt.title("Original Data Category Proportions (2014-2024)")
plt.axis('off')
plt.show()

Original category distribution:
 Category
Franchise     21.8
Reception     19.5
Industry      17.2
Genre         14.9
Marketing      8.0
Technology     5.7
Talent         4.6
Production     3.4
Narrative      2.3
Market         1.1
Social         1.1
Name: proportion, dtype: float64

/Users/heshuqing0305/anaconda3/envs/py3.9/lib/python3.9/site-packages/squarify/__init__.py:231: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.
  cmap = matplotlib.cm.get_cmap()

reception_topics = df[df['Category'] == 'Reception']

# Calculate proportion of each confidence level
reception_conf_dist = reception_topics['Confidence'].value_counts(normalize=True).sort_index()

# Print confidence distribution
print("Confidence Distribution in Reception Topics:")
print(reception_conf_dist.mul(100).round(1).astype(str) + '%')


plt.figure(figsize=(8, 5))
reception_conf_dist.mul(100).plot(kind='bar', color='skyblue')
plt.title('Confidence Distribution in Reception Topics')
plt.xlabel('Confidence Level')
plt.ylabel('Percentage (%)')
plt.xticks(rotation=0)  
plt.ylim(0, 100) 
plt.grid(axis='y', linestyle=' ', alpha=0.7) 
plt.show()

Confidence Distribution in Reception Topics:
Confidence
1    64.7%
2    29.4%
4     5.9%
Name: proportion, dtype: object

# Filter High-Confidence Data (Confidence ≥ 3)
high_conf = df[df['Confidence'] >= 3].copy()
high_conf_dist = high_conf['Category'].value_counts(normalize=True).mul(100).round(1)

print("High-Confidence (≥3) Category Distribution (%):")
print(high_conf_dist.sort_values(ascending=False))
print("\n" + "="*50 + "\n")


# Compare Distributions
comparison = pd.concat([
    original_dist.rename('Original (%)'),
    high_conf_dist.rename('High_Conf (%)')
], axis=1)

comparison['Difference (%)'] = comparison['High_Conf (%)'] - comparison['Original (%)']
comparison = comparison.sort_values('Difference (%)', ascending=False)

print("Distribution Comparison:")
print(comparison)
print("\n" + "="*50 + "\n")


# Visualization
plt.figure(figsize=(12, 6))

# Prepare data for plotting
categories = ['Franchise', 'Industry', 'Reception']
original_values = [original_dist[c] for c in categories]
high_conf_values = [high_conf_dist.get(c, 0) for c in categories]
x = np.arange(len(categories))
width = 0.35

# Create bars
bars1 = plt.bar(x - width/2, original_values, width, label='Original', color='skyblue')
bars2 = plt.bar(x + width/2, high_conf_values, width, label='High-Confidence', color='orange')

# Add labels and formatting
plt.title('Category Distribution: Original vs High-Confidence Data', pad=20)
plt.xlabel('Category', labelpad=10)
plt.ylabel('Percentage (%)', labelpad=10)
plt.xticks(x, categories)
plt.ylim(0, 30)
plt.grid(axis='y', alpha=0.3)

# Add value labels
for bars in [bars1, bars2]:
    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width()/2., height,
                f'{height}%',
                ha='center', va='bottom')

# Add difference arrows
for i, (orig, hc) in enumerate(zip(original_values, high_conf_values)):
    diff = hc - orig
    if abs(diff) > 1:  # Only show significant differences
        plt.annotate(f'{diff:+.1f}%',
                    xy=(i, max(orig, hc)),
                    xytext=(0, 10),
                    textcoords='offset points',
                    ha='center',
                    arrowprops=dict(arrowstyle='->', color='red' if diff < 0 else 'green'))

plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.savefig('high_conf_comparison.png', dpi=300, bbox_inches='tight')
plt.show()

# Key Statistics Output
print("\nKey Observations:")
print(f"- Franchise topics increase by {comparison.loc['Franchise', 'Difference (%)']:.1f}% in high-confidence data")
print(f"- Industry topics increase by {comparison.loc['Industry', 'Difference (%)']:.1f}%")
print(f"- Reception topics decrease by {-comparison.loc['Reception', 'Difference (%)']:.1f}% (mostly low-confidence)")

High-Confidence (≥3) Category Distribution (%):
Category
Franchise     27.7
Industry      21.5
Genre         16.9
Marketing      9.2
Technology     7.7
Talent         6.2
Production     4.6
Reception      1.5
Market         1.5
Narrative      1.5
Social         1.5
Name: proportion, dtype: float64

==================================================

Distribution Comparison:
            Original (%)  High_Conf (%)  Difference (%)
Category                                               
Franchise           21.8           27.7             5.9
Industry            17.2           21.5             4.3
Technology           5.7            7.7             2.0
Genre               14.9           16.9             2.0
Talent               4.6            6.2             1.6
Production           3.4            4.6             1.2
Marketing            8.0            9.2             1.2
Market               1.1            1.5             0.4
Social               1.1            1.5             0.4
Narrative            2.3            1.5            -0.8
Reception           19.5            1.5           -18.0

==================================================

Key Observations:
- Franchise topics increase by 5.9% in high-confidence data
- Industry topics increase by 4.3%
- Reception topics decrease by 18.0% (mostly low-confidence)

# Load and filter data
high_conf = df[df['Confidence'] >= 3].copy()
CORE_CATS = ['Franchise', 'Industry', 'Genre', 'Technology']

# Calculate annual trends
trends = pd.crosstab(
    index=high_conf['Year'],
    columns=high_conf['Category'],
    normalize='index'
)[CORE_CATS] * 100

# Create subplots
fig, axes = plt.subplots(2, 2, figsize=(16, 12))
plt.suptitle('High-Confidence Topic Trends (2014-2024)\nConfidence ≥ 3', y=1.02, fontsize=14)
axes = axes.flatten()

# Custom color palette
palette = {
    'Franchise': '#FF7F0E',
    'Industry': '#1F77B4', 
    'Genre': '#2CA02C',
    'Technology': '#D62728'
}

# Plot each category separately
for i, category in enumerate(CORE_CATS):
    ax = axes[i]
    
    # Main trend line
    sns.lineplot(
        x=trends.index,
        y=trends[category],
        ax=ax,
        color=palette[category],
        linewidth=2.5,
        marker='o',
        markersize=8
    )
    
    # Highlight pandemic years
    ax.axvspan(2020, 2021, color='grey', alpha=0.2)
    
    # Annotate peaks and valleys
    max_idx = trends[category].idxmax()
    min_idx = trends[category].idxmin()
    
    ax.annotate(f'Max: {trends[category].max():.1f}%', 
                xy=(max_idx, trends[category].max()),
                xytext=(5, 5), textcoords='offset points',
                bbox=dict(boxstyle='round,pad=0.5', fc='white', alpha=0.8))
    
    ax.annotate(f'Min: {trends[category].min():.1f}%',
                xy=(min_idx, trends[category].min()),
                xytext=(5, -15), textcoords='offset points',
                bbox=dict(boxstyle='round,pad=0.5', fc='white', alpha=0.8))
    
    # Formatting
    ax.set_title(f'{category} Trends', pad=10)
    ax.set_xlabel('Year')
    ax.set_ylabel('Proportion (%)')
    ax.set_xticks(range(2014, 2025))
    ax.set_ylim(0, trends[CORE_CATS].max().max() * 1.1)
    ax.grid(alpha=0.3)
    
    # Add trend line
    z = np.polyfit(trends.index, trends[category], 1)
    p = np.poly1d(z)
    ax.plot(trends.index, p(trends.index), 
            linestyle='--', 
            color=palette[category],
            alpha=0.5,
            label='Trend Line')
    ax.legend()

plt.tight_layout()
plt.savefig('separated_trends.png', dpi=300, bbox_inches='tight')
plt.show()

2.7 Processing LDA Results and Constructing a Unified Topic Matrix

import pandas as pd
import numpy as np
import ast

# Read data
original_data = pd.read_csv('/Users/heshuqing0305/Downloads/original_data.csv') 
lda_results = pd.read_csv('topic_modeling_results/document_topic_distributions.csv') 

# Parse topic distribution
def parse_dict_dist(text):
    try:
        dist_dict = ast.literal_eval(str(text))
        if isinstance(dist_dict, dict):
            n_topics = max(map(int, dist_dict.keys())) + 1
            dist_array = np.zeros(n_topics, dtype=np.float32)
            for k, v in dist_dict.items():
                dist_array[int(k)] = float(v)
            return dist_array
    except Exception as e:
        print(f"Parsing exception: {str(e)}")
    return None

# Process LDA results
lda_results['topic_dist'] = lda_results['topic_distribution'].apply(parse_dict_dist)
valid_data = lda_results.dropna(subset=['topic_dist']).copy()

# Build topic matrix
year_max_topics = valid_data.groupby('year')['topic_dist'].agg(lambda x: len(x.iloc[0])).to_dict()
all_columns = [f"{year}_Topic{i}" for year in year_max_topics for i in range(year_max_topics[year])]
doc_matrix = pd.DataFrame(0.0, index=valid_data['comment_id'], columns=all_columns)

# Fill data
for _, row in valid_data.iterrows():
    year = row['year']
    for topic_idx, prob in enumerate(row['topic_dist']):
        doc_matrix.at[row['comment_id'], f"{year}_Topic{topic_idx}"] = prob

# Merge metadata
doc_matrix.reset_index(inplace=True)
final_matrix = pd.merge(doc_matrix, original_data[['comment_id', 'Movie Title', 'comment_year']], on='comment_id', how='left')

# Create topic mapping dictionary
annotations = pd.read_csv('/Users/heshuqing0305/Downloads/Movie_Topics_Label.csv') 
topic_map = {}
for _, row in annotations.iterrows():
    topic_num = row['Topic#'].replace(" ", "")
    key = f"{row['Year']}_{topic_num}"
    topic_map[key] = row['Label']

# Generate unified topic mapping
unified_topics = {}
for col in final_matrix.columns:
    if any(col.startswith(f"{year}_Topic") for year in annotations['Year'].unique()):
        parts = col.split('_')
        if len(parts) >= 2 and parts[1].startswith('Topic'):
            year = parts[0]
            topic_num = parts[1].replace("Topic", "")
            lookup_key = f"{year}_Topic{topic_num}"
            if lookup_key in topic_map:
                label = topic_map[lookup_key]
                if label not in unified_topics:
                    unified_topics[label] = []
                unified_topics[label].append(col)

# Create new topic matrix
unified_doc_matrix = pd.DataFrame(0.0, index=final_matrix['comment_id'], columns=list(unified_topics.keys()))

# Fill new topic matrix
for index, row in final_matrix.iterrows():
    for topic_label, cols in unified_topics.items():
        unified_doc_matrix.at[row['comment_id'], topic_label] += row[cols].sum()
    
    # Print progress
    if index % 10000 == 0:
        print(f"Processed {index} entries...")

# Output document matrix information
print("unified_doc_matrix column names: ", unified_doc_matrix.columns)
print("unified_doc_matrix example: ", unified_doc_matrix.head())
print("unified_doc_matrix info: ", unified_doc_matrix.info())

Processed 0 entries...
Processed 10000 entries...
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Processed 210000 entries...
Processed 220000 entries...
unified_doc_matrix column names:  Index(['3D_IMAX', 'Sci-Fi', 'Positive', 'Action', 'Horror', 'Marketing',
       'K-Wave', 'Biography', 'Fantasy', 'Oscars', 'Box Office',
       'Co-Production', 'Anticipation', 'Viewing Habits', 'Negative',
       'Neutral', 'Superhero', 'Script Development', 'Kaiju', 'Comedy',
       'Streaming', 'Animation', 'Filmmaking', 'Political Themes', 'Narrative',
       'Fake News', 'IP Management', 'Mixed', 'Family Themes',
       'Social Diversity', 'War', 'Viewing Trends', 'CGI', 'Game Adaptation'],
      dtype='object')
unified_doc_matrix example:               3D_IMAX    Sci-Fi  Positive    Action    Horror  Marketing  \
comment_id                                                                
5           0.015880  0.190231  0.381898  0.271167  0.029295   0.030989   
24          0.000000  0.000000  0.570520  0.365656  0.000000   0.000000   
58          0.012435  0.042597  0.507401  0.217727  0.022941   0.024267   
60          0.015849  0.191520  0.381149  0.133607  0.029238   0.168173   
101         0.000000  0.032189  0.700770  0.174051  0.017595   0.018338   

              K-Wave  Biography   Fantasy  Oscars  ...  Narrative  Fake News  \
comment_id                                         ...                         
5           0.039260   0.022075  0.019205     0.0  ...        0.0        0.0   
24          0.011750   0.000000  0.000000     0.0  ...        0.0        0.0   
58          0.030980   0.126612  0.015040     0.0  ...        0.0        0.0   
60          0.039183   0.022032  0.019250     0.0  ...        0.0        0.0   
101         0.023232   0.013063  0.011365     0.0  ...        0.0        0.0   

            IP Management  Mixed  Family Themes  Social Diversity  War  \
comment_id                                                               
5                     0.0    0.0            0.0               0.0  0.0   
24                    0.0    0.0            0.0               0.0  0.0   
58                    0.0    0.0            0.0               0.0  0.0   
60                    0.0    0.0            0.0               0.0  0.0   
101                   0.0    0.0            0.0               0.0  0.0   

            Viewing Trends  CGI  Game Adaptation  
comment_id                                        
5                      0.0  0.0              0.0  
24                     0.0  0.0              0.0  
58                     0.0  0.0              0.0  
60                     0.0  0.0              0.0  
101                    0.0  0.0              0.0  

[5 rows x 34 columns]
<class 'pandas.core.frame.DataFrame'>
Index: 222520 entries, 5 to 222519
Data columns (total 34 columns):
 #   Column              Non-Null Count   Dtype  
---  ------              --------------   -----  
 0   3D_IMAX             222520 non-null  float64
 1   Sci-Fi              222520 non-null  float64
 2   Positive            222520 non-null  float64
 3   Action              222520 non-null  float64
 4   Horror              222520 non-null  float64
 5   Marketing           222520 non-null  float64
 6   K-Wave              222520 non-null  float64
 7   Biography           222520 non-null  float64
 8   Fantasy             222520 non-null  float64
 9   Oscars              222520 non-null  float64
 10  Box Office          222520 non-null  float64
 11  Co-Production       222520 non-null  float64
 12  Anticipation        222520 non-null  float64
 13  Viewing Habits      222520 non-null  float64
 14  Negative            222520 non-null  float64
 15  Neutral             222520 non-null  float64
 16  Superhero           222520 non-null  float64
 17  Script Development  222520 non-null  float64
 18  Kaiju               222520 non-null  float64
 19  Comedy              222520 non-null  float64
 20  Streaming           222520 non-null  float64
 21  Animation           222520 non-null  float64
 22  Filmmaking          222520 non-null  float64
 23  Political Themes    222520 non-null  float64
 24  Narrative           222520 non-null  float64
 25  Fake News           222520 non-null  float64
 26  IP Management       222520 non-null  float64
 27  Mixed               222520 non-null  float64
 28  Family Themes       222520 non-null  float64
 29  Social Diversity    222520 non-null  float64
 30  War                 222520 non-null  float64
 31  Viewing Trends      222520 non-null  float64
 32  CGI                 222520 non-null  float64
 33  Game Adaptation     222520 non-null  float64
dtypes: float64(34)
memory usage: 67.5 MB
unified_doc_matrix info:  None

unified_doc_matrix.info()

<class 'pandas.core.frame.DataFrame'>
Index: 222520 entries, 5 to 222519
Data columns (total 34 columns):
 #   Column              Non-Null Count   Dtype  
---  ------              --------------   -----  
 0   3D_IMAX             222520 non-null  float64
 1   Sci-Fi              222520 non-null  float64
 2   Positive            222520 non-null  float64
 3   Action              222520 non-null  float64
 4   Horror              222520 non-null  float64
 5   Marketing           222520 non-null  float64
 6   K-Wave              222520 non-null  float64
 7   Biography           222520 non-null  float64
 8   Fantasy             222520 non-null  float64
 9   Oscars              222520 non-null  float64
 10  Box Office          222520 non-null  float64
 11  Co-Production       222520 non-null  float64
 12  Anticipation        222520 non-null  float64
 13  Viewing Habits      222520 non-null  float64
 14  Negative            222520 non-null  float64
 15  Neutral             222520 non-null  float64
 16  Superhero           222520 non-null  float64
 17  Script Development  222520 non-null  float64
 18  Kaiju               222520 non-null  float64
 19  Comedy              222520 non-null  float64
 20  Streaming           222520 non-null  float64
 21  Animation           222520 non-null  float64
 22  Filmmaking          222520 non-null  float64
 23  Political Themes    222520 non-null  float64
 24  Narrative           222520 non-null  float64
 25  Fake News           222520 non-null  float64
 26  IP Management       222520 non-null  float64
 27  Mixed               222520 non-null  float64
 28  Family Themes       222520 non-null  float64
 29  Social Diversity    222520 non-null  float64
 30  War                 222520 non-null  float64
 31  Viewing Trends      222520 non-null  float64
 32  CGI                 222520 non-null  float64
 33  Game Adaptation     222520 non-null  float64
dtypes: float64(34)
memory usage: 67.5 MB

analysis_matrix = pd.merge(
    unified_doc_matrix.reset_index(),
    original_data[['comment_id', 'Movie Title', 'comment_year']],
    on='comment_id',
    how='left'
)

analysis_matrix.to_csv('analysis_matrix.csv', index=False, quoting=1)

analysis_matrix = pd.read_csv('analysis_matrix.csv', skip_blank_lines=True)

2.6.3 Trends in Franchise Movie Discussions

analysis_matrix.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 222520 entries, 0 to 222519
Data columns (total 37 columns):
 #   Column              Non-Null Count   Dtype  
---  ------              --------------   -----  
 0   comment_id          222520 non-null  int64  
 1   3D_IMAX             222520 non-null  float64
 2   Sci-Fi              222520 non-null  float64
 3   Positive            222520 non-null  float64
 4   Action              222520 non-null  float64
 5   Horror              222520 non-null  float64
 6   Marketing           222520 non-null  float64
 7   K-Wave              222520 non-null  float64
 8   Biography           222520 non-null  float64
 9   Fantasy             222520 non-null  float64
 10  Oscars              222520 non-null  float64
 11  Box Office          222520 non-null  float64
 12  Co-Production       222520 non-null  float64
 13  Anticipation        222520 non-null  float64
 14  Viewing Habits      222520 non-null  float64
 15  Negative            222520 non-null  float64
 16  Neutral             222520 non-null  float64
 17  Superhero           222520 non-null  float64
 18  Script Development  222520 non-null  float64
 19  Kaiju               222520 non-null  float64
 20  Comedy              222520 non-null  float64
 21  Streaming           222520 non-null  float64
 22  Animation           222520 non-null  float64
 23  Filmmaking          222520 non-null  float64
 24  Political Themes    222520 non-null  float64
 25  Narrative           222520 non-null  float64
 26  Fake News           222520 non-null  float64
 27  IP Management       222520 non-null  float64
 28  Mixed               222520 non-null  float64
 29  Family Themes       222520 non-null  float64
 30  Social Diversity    222520 non-null  float64
 31  War                 222520 non-null  float64
 32  Viewing Trends      222520 non-null  float64
 33  CGI                 222520 non-null  float64
 34  Game Adaptation     222520 non-null  float64
 35  Movie Title         222520 non-null  object 
 36  comment_year        222520 non-null  int64  
dtypes: float64(34), int64(2), object(1)
memory usage: 62.8+ MB

annotations.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 87 entries, 0 to 86
Data columns (total 7 columns):
 #   Column            Non-Null Count  Dtype 
---  ------            --------------  ----- 
 0   Year              87 non-null     int64 
 1   Topic#            87 non-null     object
 2   Keywords          87 non-null     object
 3   Specific Context  87 non-null     object
 4   Series            87 non-null     object
 5   Label             87 non-null     object
 6   Category          87 non-null     object
dtypes: int64(1), object(6)
memory usage: 4.9+ KB

for column in annotations.columns:
    unique_values = annotations[column].unique()
    print(f"Unique values in column '{column}': {unique_values}")

Unique values in column 'Year': [2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024]
Unique values in column 'Topic#': ['Topic 0' 'Topic 1' 'Topic 2' 'Topic 3' 'Topic 4' 'Topic 5' 'Topic 6'
 'Topic 7' 'Topic 8' 'Topic 9']
Unique values in column 'Keywords': ['3d_technology, imax' 'apes, planet' 'really, good' 'action, fight'
 'sony, transformers' 'horror, woman' 'release, review' 'tv, korea'
 'cruise, turing' 'disney, maleficent' 'mad, fury' 'poster, game'
 'oscar, award' 'martian, summer' 'animate, interstellar' 'horror, depp'
 'bond, james' 'star_wars, 3d' 'really, movies' 'oscar, win'
 'gross, shark' 'chinese, hunt' 'giant, army' 'movies, hope'
 'really, watch' 'watch, scene' 'fucking, ass' 'really, people'
 'marvel, deadpool' 'oscar, picture' 'star_wars, logan' 'character, story'
 'budget, box' 'john, wick' 'star_wars, jedi' 'black_panther'
 'really, look' 'budget, marvel' 'godzilla, doctor' 'awards, sex'
 'die, space' 'parasite, zombie' 'jojo, rabbit' 'stream, ruin'
 'trolls, witch' 'directors, feature' 'office, star' 'king, america'
 'marvel, cats' 'netflix, kid' 'title, twist' 'trucks, halloween'
 'strange, news' 'kill, space' 'disney, bond, taskmaster' 'watch, trailer'
 'godzilla, kong' 'rock, heist' 'really, take' 'spiderman, matrix'
 'poster, minions' 'marvel, batman' 'strange, squad' 'family, life'
 'imax, avatar' 'sonic, art' 'elvis, king' 'really, love' 'black, school'
 'napoleon, wars' 'marvel, antman' 'voice, box' 'go, people'
 'godzilla, oppenheimer' 'really, feel' 'vfx, oppenheimer, cgi'
 'watch, work' 'music, game' 'robot, ai' 'love, feel' 'gosling, lion'
 'godzilla, dune']
Unique values in column 'Specific Context': ['3D/IMAX technology discussion' 'Dawn of the Planet of the Apes'
 'General positive reviews' 'Action movie elements' 'Transformers 4'
 'Female-led horror films' 'Film marketing' 'Korean media influence'
 'The Imitation Game' 'Maleficent' 'Mad Max: Fury Road' 'Movie marketing'
 'Awards season' 'The Martian' 'Jupiter Ascending'
 'Johnny Depp horror films' 'Spectre' 'Star Wars: The Force Awakens'
 'General appreciation' 'The Revenant awards' 'The Shallows box office'
 'Sino-Hollywood co-productions' 'The BFG' 'Anticipation for new films'
 'Positive viewing' 'Viewing patterns' 'Negative reactions'
 'Neutral discussions' 'MCU and Deadpool' 'Moonlight awards'
 'Star Wars and Logan releases' 'Screenwriting' 'Box office analysis'
 'John Wick 2' 'The Last Jedi' 'Black Panther' 'Neutral analysis'
 'Captain Marvel budget' 'General discussions'
 'Godzilla: King of the Monsters' 'John Wick 3' 'Awards controversies'
 'Ad Astra' 'Parasite awards' 'Jojo Rabbit' 'Streaming impact'
 'Trolls World Tour' 'Filmmaking process' 'Box office performance'
 'Audience behavior' 'Political Themes in Films' 'MCU delays'
 'Netflix films' 'Narrative devices' 'Halloween Kills'
 'Fake News in Entertainment' 'Sci-fi violence'
 'Disney corporate challenges' 'Anticipation' 'Godzilla vs Kong'
 'Fast & Furious 9' 'Mixed critiques' 'Spider-Man: No Way Home'
 'Minions 2 marketing' 'The Batman' 'Doctor Strange 2'
 'Family Themes in Films' 'Avatar 2 technology' 'Sonic the Hedgehog 2'
 'Elvis biopic' 'Positive reviews' 'The Color Purple' 'Napoleon biopic'
 'Ant-Man 3' 'Avatar re-release' 'John Wick 4' 'Voice acting'
 'Post-pandemic viewing' 'Godzilla x Oppenheimer' 'Emotional responses'
 'Visual effects tech' 'Hybrid viewing' 'Video game adaptations'
 'AI-themed films' 'Emotional connections' 'The Fall Guy'
 'Godzilla x Kong 2']
Unique values in column 'Series': ['-' 'Planet of the Apes' 'Transformers' 'Disney' 'Mad Max' 'James Bond'
 'Star Wars' 'Marvel' 'John Wick' 'MonsterVerse' 'Trolls' 'Netflix'
 'Fast & Furious' 'Minions' 'DC' 'Avatar' 'Sonic']
Unique values in column 'Label': ['3D_IMAX' 'Sci-Fi' 'Positive' 'Action' 'Horror' 'Marketing' 'K-Wave'
 'Biography' 'Fantasy' 'Oscars' 'Box Office' 'Co-Production'
 'Anticipation' 'Viewing Habits' 'Negative' 'Neutral' 'Superhero'
 'Script Development' 'Kaiju' 'Comedy' 'Streaming' 'Animation'
 'Filmmaking' 'Political Themes' 'Narrative' 'Fake News' 'IP Management'
 'Mixed' 'Family Themes' 'Social Diversity' 'War' 'Viewing Trends' 'CGI'
 'Game Adaptation']
Unique values in column 'Category': ['Film_Tech' 'Franchise' 'Audience_Response' 'Genre' 'Industry'
 'Social_Context']

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# ======================
# Step 1: Data Loading
# ======================
print("STEP 1: DATA LOADING")
print("="*50)

df = pd.read_csv('analysis_matrix.csv')
print(f"Data range: {df['comment_year'].min()} to {df['comment_year'].max()}")

# ======================
# Step 2: Analysis Setup
# ======================
print("\nSTEP 2: ANALYSIS PARAMETERS")
print("="*50)

franchise_labels = ['Sci-Fi', 'Fantasy', 'Action', 'Superhero', 'Kaiju', 'Animation']
print(f"Target labels: {franchise_labels}")

# ======================
# Step 3: Yearly Trends
# ======================
print("\nSTEP 3: COMPUTING YEARLY TRENDS")
print("="*50)

df = df[df['comment_year'].between(2014, 2024)]
trends = df.groupby('comment_year')[franchise_labels].mean()

print("\nYearly Trend Data (2014-2024):")
display(trends.style.format("{:.4f}").background_gradient(cmap='Blues'))

# ======================
# Step 4: Visual Analysis
# ======================
print("\nSTEP 4: VISUALIZATION")
print("="*50)

plt.figure(figsize=(14, 12))
for i, label in enumerate(franchise_labels, 1):
    plt.subplot(3, 2, i)
    plt.plot(trends.index, trends[label], color='steelblue', marker='o', linewidth=2)
    plt.title(f'{label} Trend', fontsize=12)
    plt.xlabel('Year', fontsize=10)
    plt.ylabel('Discussion Intensity', fontsize=10)
    plt.ylim(0, trends.max().max()*1.1)
    plt.grid(alpha=0.2)
    
    # Annotate peak year
    peak_year = trends[label].idxmax()
    peak_val = trends.loc[peak_year, label]
    plt.annotate(f'Peak: {peak_year}\n({peak_val:.3f})', 
                xy=(peak_year, peak_val),
                xytext=(10, 10), 
                textcoords='offset points',
                arrowprops=dict(arrowstyle='->'))

plt.tight_layout()
plt.show()

# ======================
# Step 5: Key Metrics
# ======================
print("\nSTEP 5: KEY METRICS")
print("="*50)

print("\nPeak Years for Each Genre:")
display(trends.idxmax().to_frame('Peak Year').style.set_caption('Genre Peak Years'))

print("\nCorrelation Matrix:")
corr_matrix = trends.corr()
display(corr_matrix.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)\
                      .format("{:.2f}")\
                      .set_caption('Genre Correlations (Pearson)'))

# ======================
# Step 6: Data Export
# ======================
print("\nSTEP 6: EXPORTING RESULTS")
print("="*50)
trends.to_csv('genre_trends_2014_2024.csv')
print("Trend data saved to 'genre_trends_2014_2024.csv'")

STEP 1: DATA LOADING
==================================================
Data range: 2014 to 2024

STEP 2: ANALYSIS PARAMETERS
==================================================
Target labels: ['Sci-Fi', 'Fantasy', 'Action', 'Superhero', 'Kaiju', 'Animation']

STEP 3: COMPUTING YEARLY TRENDS
==================================================

Yearly Trend Data (2014-2024):

	Sci-Fi	Fantasy	Action	Superhero	Kaiju	Animation
comment_year
2014	0.0753	0.0251	0.1785	0.0000	0.0000	0.0000
2015	0.0867	0.0000	0.0912	0.0000	0.0000	0.0000
2016	0.0000	0.0315	0.0000	0.0000	0.0000	0.0000
2017	0.0698	0.0000	0.0000	0.0486	0.0000	0.0000
2018	0.0483	0.0000	0.0512	0.0653	0.0000	0.0000
2019	0.0759	0.0000	0.0628	0.0950	0.0414	0.0000
2020	0.0000	0.0000	0.0000	0.0135	0.0000	0.0258
2021	0.0320	0.0000	0.0479	0.0268	0.0235	0.0000
2022	0.0309	0.0000	0.0000	0.0799	0.0000	0.0807
2023	0.0371	0.0000	0.0278	0.0276	0.0547	0.0304
2024	0.0346	0.0000	0.0000	0.0000	0.0508	0.0000

STEP 4: VISUALIZATION
==================================================

STEP 5: KEY METRICS
==================================================

Peak Years for Each Genre:

Genre Peak Years

	Peak Year
Sci-Fi	2015
Fantasy	2016
Action	2014
Superhero	2019
Kaiju	2023
Animation	2022

Correlation Matrix:

Genre Correlations (Pearson)

	Sci-Fi	Fantasy	Action	Superhero	Kaiju	Animation
Sci-Fi	1.00	-0.19	0.65	0.21	0.01	-0.33
Fantasy	-0.19	1.00	0.33	-0.46	-0.33	-0.24
Action	0.65	0.33	1.00	-0.18	-0.15	-0.35
Superhero	0.21	-0.46	-0.18	1.00	0.07	0.36
Kaiju	0.01	-0.33	-0.15	0.07	1.00	-0.08
Animation	-0.33	-0.24	-0.35	0.36	-0.08	1.00

STEP 6: EXPORTING RESULTS
==================================================
Trend data saved to 'genre_trends_2014_2024.csv'

marvel_trend

comment_year
2017    0.048558
2018    0.065280
2019    0.094966
2020    0.013484
2021    0.026766
2022    0.079851
2023    0.027644
Name: Superhero, dtype: float64

top_series = franchise_series['Series'].value_counts().index[:5]

def compare_series(series_list):
    """Compare trends of multiple franchises."""
    plt.figure(figsize=(12, 6))
    
    for series in series_list:
        series_data = franchise_series[franchise_series['Series'] == series]
        label = series_data['Label'].iloc[0]
        
        # Merge data to compute trends
        merged = pd.merge(df, series_data, left_on='comment_year', right_on='Year')
        trend = merged.groupby('comment_year')[label].mean()

        # Print the trend data for each series
        print(f"\nTrend data for {series}:\n", trend)

        plt.plot(trend.index, trend, label=series, marker='o')

    plt.title('Franchise Series Comparison')
    plt.ylabel('Discussion Intensity')
    plt.xlabel('Year')
    plt.legend()
    plt.grid(False)
    plt.tight_layout()
    plt.show()

# Example: Compare major franchises
compare_series(['Marvel', 'Star Wars', 'John Wick', 'MonsterVerse', 'Avatar'])

Trend data for Marvel:
 comment_year
2017    0.048558
2018    0.065280
2019    0.094966
2020    0.013484
2021    0.026766
2022    0.079851
2023    0.027644
Name: Superhero, dtype: float64

Trend data for Star Wars:
 comment_year
2015    0.086678
2017    0.069825
2018    0.048291
Name: Sci-Fi, dtype: float64

Trend data for John Wick:
 comment_year
2018    0.051150
2019    0.062824
2023    0.027825
Name: Action, dtype: float64

Trend data for MonsterVerse:
 comment_year
2019    0.041398
2021    0.023513
2023    0.054663
2024    0.050791
Name: Kaiju, dtype: float64

Trend data for Avatar:
 comment_year
2022    0.030922
2023    0.037090
Name: Sci-Fi, dtype: float64

2.6.4 Trends in Film Technology Discussions

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# ======================
# Step 1: Data Loading
# ======================
print("STEP 1: DATA LOADING")
print("="*50)

df = pd.read_csv('analysis_matrix.csv')
print(f"Data range: {df['comment_year'].min()} to {df['comment_year'].max()}")

# ======================
# Step 2: Analysis Setup
# ======================
print("\nSTEP 2: ANALYSIS PARAMETERS")
print("="*50)

tech_labels = ['3D_IMAX', 'CGI', 'Streaming']  # Film technology-related labels
print(f"Target technology labels: {tech_labels}")

# Define technology milestone events (adding a complete event timeline)
tech_milestones = {
    2014: '3D Technology Peak',
    2019: 'Netflix Dominance\n(152M subscribers)',
    2020: 'Pandemic Streaming Boom\n(Disney+ launch)',
    2022: 'Avatar 2\nUnderwater CGI Breakthrough',
    2024: 'AI-Assisted CGI\nEmerging Technologies'
}

# ======================
# Step 3: Yearly Trends
# ======================
print("\nSTEP 3: COMPUTING YEARLY TRENDS")
print("="*50)

df = df[df['comment_year'].between(2014, 2024)]
tech_trends = df.groupby('comment_year')[tech_labels].mean()

print("\nTechnology Trend Data (2014-2024):")
display(tech_trends.style.format("{:.4f}").background_gradient(cmap='Blues'))

# ======================
# Step 4: Visual Analysis - Enhanced with Milestones
# ======================
print("\nSTEP 4: VISUALIZATION - WITH MILESTONES")
print("="*50)

plt.figure(figsize=(14, 7))

# Define colors and line styles
colors = ['#1f77b4', '#ff7f0e', '#2ca02c']  # Blue, Orange, Green
line_styles = ['-', '--', '-.']

# Draw three trend lines
for i, tech in enumerate(tech_labels):
    line = plt.plot(tech_trends.index, 
                   tech_trends[tech], 
                   color=colors[i],
                   linestyle=line_styles[i],
                   marker='o',
                   linewidth=2.5,
                   markersize=9,
                   label=tech,
                   zorder=5)
    
    # Annotate peaks (only for years with data)
    if tech_trends[tech].max() > 0:
        peak_year = tech_trends[tech].idxmax()
        peak_val = tech_trends.loc[peak_year, tech]
        plt.annotate(f'{peak_year}: {peak_val:.3f}',
                    xy=(peak_year, peak_val),
                    xytext=(8, 8),
                    textcoords='offset points',
                    bbox=dict(boxstyle='round,pad=0.4', fc='white', alpha=0.9),
                    fontsize=10,
                    zorder=6)

# Add milestone events (new section)
for year, event in tech_milestones.items():
    if year in tech_trends.index:
        plt.axvline(x=year, color='gray', linestyle=':', alpha=0.7, zorder=1)
        plt.text(x=year, y=plt.ylim()[1]*0.95,
                s=f'{year}: {event}',
                ha='center',
                va='top',
                rotation=90,
                bbox=dict(facecolor='white', edgecolor='gray', boxstyle='round,pad=0.3'),
                fontsize=9,
                zorder=4)

# Chart decorations
plt.title('Film Technology Discussion Trends with Key Milestones (2014-2024)', 
          fontsize=15, pad=25)
plt.xlabel('Year', fontsize=12)
plt.ylabel('Discussion Intensity', fontsize=12)
plt.legend(fontsize=12, framealpha=0.9, loc='upper right')
plt.grid(True, alpha=0.2)
plt.xticks(tech_trends.index)  # Show all years

# Adjust y-axis range to leave space for event annotations
plt.ylim(top=plt.ylim()[1]*1.1)

plt.tight_layout()
plt.show()

# ======================
# Step 5: Data Export
# ======================
print("\nSTEP 5: EXPORTING RESULTS")
print("="*50)
tech_trends.to_csv('tech_trends_2014_2024.csv')
print("Technology trend data saved to 'tech_trends_2014_2024.csv'")

STEP 1: DATA LOADING
==================================================
Data range: 2014 to 2024

STEP 2: ANALYSIS PARAMETERS
==================================================
Target technology labels: ['3D_IMAX', 'CGI', 'Streaming']

STEP 3: COMPUTING YEARLY TRENDS
==================================================

Technology Trend Data (2014-2024):

	3D_IMAX	CGI	Streaming
comment_year
2014	0.0199	0.0000	0.0000
2015	0.0000	0.0000	0.0000
2016	0.0000	0.0000	0.0000
2017	0.0000	0.0000	0.0000
2018	0.0000	0.0000	0.0000
2019	0.0000	0.0000	0.0000
2020	0.0000	0.0000	0.1633
2021	0.0000	0.0000	0.0000
2022	0.0000	0.0000	0.0000
2023	0.0000	0.0000	0.0000
2024	0.0000	0.0541	0.0000

STEP 4: VISUALIZATION - WITH MILESTONES
==================================================

STEP 5: EXPORTING RESULTS
==================================================
Technology trend data saved to 'tech_trends_2014_2024.csv'

annotations

	Year	Topic#	Keywords	Specific Context	Series	Label	Category
0	2014	Topic 0	3d_technology, imax	3D/IMAX technology discussion	-	3D_IMAX	Film_Tech
1	2014	Topic 1	apes, planet	Dawn of the Planet of the Apes	Planet of the Apes	Sci-Fi	Franchise
2	2014	Topic 2	really, good	General positive reviews	-	Positive	Audience_Response
3	2014	Topic 3	action, fight	Action movie elements	-	Action	Genre
4	2014	Topic 4	sony, transformers	Transformers 4	Transformers	Sci-Fi	Franchise
...	...	...	...	...	...	...	...
82	2024	Topic 2	music, game	Video game adaptations	-	Game Adaptation	Genre
83	2024	Topic 3	robot, ai	AI-themed films	-	Sci-Fi	Genre
84	2024	Topic 4	love, feel	Emotional connections	-	Positive	Audience_Response
85	2024	Topic 5	gosling, lion	The Fall Guy	-	Comedy	Genre
86	2024	Topic 6	godzilla, dune	Godzilla x Kong 2	MonsterVerse	Kaiju	Franchise

87 rows × 7 columns

2.6.5 Trends in Movie Topic Categories

categories = ['Film_Tech', 'Franchise', 'Audience_Response', 'Genre', 'Industry', 'Social_Context']
yearly_trends = annotations.groupby('Year')['Category'].value_counts(normalize=True).unstack().fillna(0)

for cat in categories:
    if cat not in yearly_trends.columns:
        yearly_trends[cat] = 0.0

yearly_trends = yearly_trends[categories]  # Standardize column order

print("\nAnnual Trend Data:")
display(yearly_trends.style.background_gradient(cmap='Blues', axis=0).format("{:.2%}"))


# Custom color palette
palette = {
    'Film_Tech': '#D62728',
    'Franchise': '#FF7F0E',
    'Audience_Response': '#9467BD',
    'Genre': '#2CA02C',
    'Industry': '#1F77B4',
    'Social_Context': '#8C564B'
}

# Create 6 individual plots
for category in categories:
    plt.figure(figsize=(10, 6))
    
    # Main trend line
    ax = sns.lineplot(
        x=yearly_trends.index,
        y=yearly_trends[category],
        color=palette[category],
        linewidth=2.5,
        marker='o',
        markersize=8
    )
    
    # Highlight COVID period (2020-2021)
    ax.axvspan(2020, 2021, color='grey', alpha=0.2, label='COVID Period')
    
    # Find and annotate max value
    max_val = yearly_trends[category].max()
    max_year = yearly_trends[category].idxmax()
    ax.scatter(max_year, max_val, color='red', s=100, zorder=5)
    ax.annotate(f'MAX: {max_val:.1%} ({max_year})', 
                xy=(max_year, max_val),
                xytext=(10, 10),
                textcoords='offset points',
                bbox=dict(boxstyle='round,pad=0.5', fc='white', ec='red', alpha=0.8))
    
    # Formatting
    plt.title(f'{category} Discussion Trend (2014-2024)', fontsize=14)
    plt.xlabel('Year', fontsize=12)
    plt.ylabel('Discussion Proportion', fontsize=12)
    plt.xticks(range(2014, 2025))
    plt.ylim(0, yearly_trends.max().max() * 1.1)
    plt.grid(alpha=0.3)
    plt.legend()
    
    plt.tight_layout()
    plt.savefig(f'{category}_trend.png', dpi=300, bbox_inches='tight')
    plt.show()

Annual Trend Data:

Category	Film_Tech	Franchise	Audience_Response	Genre	Industry	Social_Context
Year
2014	10.00%	30.00%	10.00%	30.00%	20.00%	0.00%
2015	0.00%	33.33%	11.11%	33.33%	22.22%	0.00%
2016	0.00%	0.00%	33.33%	16.67%	50.00%	0.00%
2017	0.00%	33.33%	50.00%	0.00%	16.67%	0.00%
2018	0.00%	50.00%	16.67%	0.00%	33.33%	0.00%
2019	0.00%	50.00%	16.67%	16.67%	16.67%	0.00%
2020	20.00%	20.00%	10.00%	10.00%	30.00%	10.00%
2021	0.00%	30.00%	20.00%	30.00%	10.00%	10.00%
2022	0.00%	62.50%	12.50%	12.50%	0.00%	12.50%
2023	0.00%	44.44%	22.22%	22.22%	0.00%	11.11%
2024	14.29%	14.29%	28.57%	42.86%	0.00%	0.00%

2.6.6 Trends in Social Issue Discussions

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

# Load data
df = pd.read_csv('analysis_matrix.csv')
print(f"Data range: {df['comment_year'].min()} to {df['comment_year'].max()}")
print(f"Total comments: {len(df):,}")

# Social theme labels
social_labels = ['Political Themes', 'Fake News', 'Family Themes', 'Social Diversity']
print(f"Target social themes: {social_labels}")

# Milestone events
milestones = {
    'Political Themes': {
        2020: 'Pandemic Cinema Shift\n("Nomadland" virtual release)',
    },
    'Fake News': {
        2020: 'Pandemic Cinema Shift\n("Nomadland" virtual release)',
        2021: 'Superhero Media Circus\n("No Way Home" leaks & "Don\'t Look Up" satire)',
    },
    'Family Themes': {
        2019: 'Streaming Wars Begin\n(Disney+, Apple TV+ launch)',
        2020: 'Pandemic Cinema Shift\n("Nomadland" virtual release)',
    },
    'Social Diversity': {
        2023: 'Feminist Discourse Peak\n("Barbie" cultural impact)',
    }
}

# Data processing
df = df[df['comment_year'].between(2014, 2024)]
trends = df.groupby('comment_year')[social_labels].mean() * 100  # Convert to percentage

# Visualization
plt.figure(figsize=(16, 14))  # Increase overall height
colors = sns.color_palette("Set2", len(social_labels))  # Use Seaborn color palette

for i, label in enumerate(social_labels):
    ax = plt.subplot(2, 2, i + 1)

    # Main trend line
    ax.plot(trends.index, trends[label],
            color=colors[i],
            linewidth=3,
            marker='o',
            markersize=8,
            label=label)

    # Annotate peak value
    peak_year = trends[label].idxmax()
    peak_val = trends[label].max()
    ax.scatter(peak_year, peak_val, color='red', s=100, zorder=5)
    ax.annotate(f'Max: {peak_val:.1f}%',
                xy=(peak_year, peak_val),
                xytext=(0, 10),
                textcoords='offset points',
                fontsize=10,
                ha='center',
                bbox=dict(boxstyle='round,pad=0.4', fc='white', alpha=0.8))

    # Add milestone events
    for year, event in milestones[label].items():
        if year in trends.index:
            y_val = trends.loc[year, label]
            ax.scatter(year, y_val, color='purple', s=120, marker='*', zorder=5)
            ax.annotate(event,
                        xy=(year, y_val),
                        xytext=(year, y_val + 15),  # Increase vertical offset
                        textcoords='data',
                        fontsize=8,
                        ha='center',
                        arrowprops=dict(arrowstyle='->', color='grey', linewidth=1.5),
                        bbox=dict(boxstyle='round,pad=0.4', fc='lightyellow', alpha=0.7))

    # Format settings
    ax.set_title(label, fontsize=16, pad=10)
    ax.set_xlabel('Year', fontsize=14)
    ax.set_ylabel('Discussion %', fontsize=14)
    ax.set_xticks(range(2014, 2025, 2))
    ax.set_ylim(0, trends.max().max() * 1.1)
    ax.grid(alpha=0.3, linestyle='--')

    # Add trend line
    if len(trends) > 1:  # Ensure there are enough data points
        z = np.polyfit(trends.index, trends[label], 1)
        p = np.poly1d(z)
        ax.plot(trends.index, p(trends.index),
                color='gray',
                linestyle='--',
                alpha=0.5,
                label='Trend')
    ax.legend(fontsize=10)

plt.tight_layout()
plt.savefig('social_trends_2019-2024_improved.png', dpi=300, bbox_inches='tight')
plt.show()

print("\nPeak Discussion Years:")
peak_years = trends.idxmax().to_frame('Peak Year')
peak_years['Max %'] = trends.max().round(1)
display(peak_years.style.set_caption('Peak Discussion by Theme'))

trends.to_csv('social_trends_focused_2014-2024.csv')
print("Saved to 'social_trends_focused_2014-2024.csv'")

Data range: 2014 to 2024
Total comments: 222,520
Target social themes: ['Political Themes', 'Fake News', 'Family Themes', 'Social Diversity']

Peak Discussion Years:

Peak Discussion by Theme

	Peak Year	Max %
Political Themes	2020	1.600000
Fake News	2021	4.000000
Family Themes	2022	20.000000
Social Diversity	2023	3.500000

Saved to 'social_trends_focused_2014-2024.csv'

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
from textwrap import wrap
import squarify
from scipy import stats
import seaborn as sns
import re

# Read dataset
df = pd.read_csv('/Users/heshuqing0305/Downloads/Movie_Topics_Label.csv')

# Check missing value
print(df.isnull().sum())

# Number of topics by year
topic_counts = df.groupby('Year')['Topic#'].count()
print(topic_counts)

Year                0
Topic#              0
Keywords            0
Specific Context    0
Series              0
Label               0
Category            0
dtype: int64
Year
2014    10
2015     9
2016     6
2017     6
2018     6
2019     6
2020    10
2021    10
2022     8
2023     9
2024     7
Name: Topic#, dtype: int64

df

	Year	Topic#	Keywords	Specific Context	Series	Label	Category
0	2014	Topic 0	3d_technology, imax	3D/IMAX technology discussion	-	3D_IMAX	Film_Tech
1	2014	Topic 1	apes, planet	Dawn of the Planet of the Apes	Planet of the Apes	Sci-Fi	Franchise
2	2014	Topic 2	really, good	General positive reviews	-	Positive	Audience_Response
3	2014	Topic 3	action, fight	Action movie elements	-	Action	Genre
4	2014	Topic 4	sony, transformers	Transformers 4	Transformers	Sci-Fi	Franchise
...	...	...	...	...	...	...	...
82	2024	Topic 2	music, game	Video game adaptations	-	Game Adaptation	Genre
83	2024	Topic 3	robot, ai	AI-themed films	-	Sci-Fi	Genre
84	2024	Topic 4	love, feel	Emotional connections	-	Positive	Audience_Response
85	2024	Topic 5	gosling, lion	The Fall Guy	-	Comedy	Genre
86	2024	Topic 6	godzilla, dune	Godzilla x Kong 2	MonsterVerse	Kaiju	Franchise

87 rows × 7 columns

df.groupby('Year')['Topic#'].count().plot(kind='bar', title='Categories per Year')
plt.xlabel('Year')
plt.ylabel('Categories Count')

Text(0, 0.5, 'Categories Count')

df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 222520 entries, 0 to 222519
Data columns (total 37 columns):
 #   Column              Non-Null Count   Dtype  
---  ------              --------------   -----  
 0   comment_id          222520 non-null  int64  
 1   3D_IMAX             222520 non-null  float64
 2   Sci-Fi              222520 non-null  float64
 3   Positive            222520 non-null  float64
 4   Action              222520 non-null  float64
 5   Horror              222520 non-null  float64
 6   Marketing           222520 non-null  float64
 7   K-Wave              222520 non-null  float64
 8   Biography           222520 non-null  float64
 9   Fantasy             222520 non-null  float64
 10  Oscars              222520 non-null  float64
 11  Box Office          222520 non-null  float64
 12  Co-Production       222520 non-null  float64
 13  Anticipation        222520 non-null  float64
 14  Viewing Habits      222520 non-null  float64
 15  Negative            222520 non-null  float64
 16  Neutral             222520 non-null  float64
 17  Superhero           222520 non-null  float64
 18  Script Development  222520 non-null  float64
 19  Kaiju               222520 non-null  float64
 20  Comedy              222520 non-null  float64
 21  Streaming           222520 non-null  float64
 22  Animation           222520 non-null  float64
 23  Filmmaking          222520 non-null  float64
 24  Political Themes    222520 non-null  float64
 25  Narrative           222520 non-null  float64
 26  Fake News           222520 non-null  float64
 27  IP Management       222520 non-null  float64
 28  Mixed               222520 non-null  float64
 29  Family Themes       222520 non-null  float64
 30  Social Diversity    222520 non-null  float64
 31  War                 222520 non-null  float64
 32  Viewing Trends      222520 non-null  float64
 33  CGI                 222520 non-null  float64
 34  Game Adaptation     222520 non-null  float64
 35  Movie Title         222520 non-null  object 
 36  comment_year        222520 non-null  int64  
dtypes: float64(34), int64(2), object(1)
memory usage: 62.8+ MB

2.6.7 Trends in Box Office and Audience Discussion

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import statsmodels.api as sm

df = pd.read_csv('analysis_matrix.csv')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 222520 entries, 0 to 222519
Data columns (total 37 columns):
 #   Column              Non-Null Count   Dtype  
---  ------              --------------   -----  
 0   comment_id          222520 non-null  int64  
 1   3D_IMAX             222520 non-null  float64
 2   Sci-Fi              222520 non-null  float64
 3   Positive            222520 non-null  float64
 4   Action              222520 non-null  float64
 5   Horror              222520 non-null  float64
 6   Marketing           222520 non-null  float64
 7   K-Wave              222520 non-null  float64
 8   Biography           222520 non-null  float64
 9   Fantasy             222520 non-null  float64
 10  Oscars              222520 non-null  float64
 11  Box Office          222520 non-null  float64
 12  Co-Production       222520 non-null  float64
 13  Anticipation        222520 non-null  float64
 14  Viewing Habits      222520 non-null  float64
 15  Negative            222520 non-null  float64
 16  Neutral             222520 non-null  float64
 17  Superhero           222520 non-null  float64
 18  Script Development  222520 non-null  float64
 19  Kaiju               222520 non-null  float64
 20  Comedy              222520 non-null  float64
 21  Streaming           222520 non-null  float64
 22  Animation           222520 non-null  float64
 23  Filmmaking          222520 non-null  float64
 24  Political Themes    222520 non-null  float64
 25  Narrative           222520 non-null  float64
 26  Fake News           222520 non-null  float64
 27  IP Management       222520 non-null  float64
 28  Mixed               222520 non-null  float64
 29  Family Themes       222520 non-null  float64
 30  Social Diversity    222520 non-null  float64
 31  War                 222520 non-null  float64
 32  Viewing Trends      222520 non-null  float64
 33  CGI                 222520 non-null  float64
 34  Game Adaptation     222520 non-null  float64
 35  Movie Title         222520 non-null  object 
 36  comment_year        222520 non-null  int64  
dtypes: float64(34), int64(2), object(1)
memory usage: 62.8+ MB

df1 = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv')

df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1095 entries, 0 to 1094
Data columns (total 11 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Title              1095 non-null   object 
 1   Year               1095 non-null   int64  
 2   Director           1095 non-null   object 
 3   Rating             1095 non-null   float64
 4   Votes              1095 non-null   int64  
 5   Genres             1095 non-null   object 
 6   Actors             1095 non-null   object 
 7   Languages          1095 non-null   object 
 8   Countries          1095 non-null   object 
 9   Budget_Amount      1095 non-null   int64  
 10  Box_Office_Amount  1095 non-null   int64  
dtypes: float64(1), int64(4), object(6)
memory usage: 94.2+ KB

df = pd.read_csv('analysis_matrix.csv') # LDA topic distributions
df1 = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv') # Movie metadata


topic_columns = df.columns[1:-2] # Excluding metadata columns
movie_topic_heat = df.groupby('Movie Title')[topic_columns].mean().reset_index()


movie_box_office = df1[['Title', 'Box_Office_Amount']].rename(columns={'Title': 'Movie Title'})
merged_data = movie_topic_heat.merge(movie_box_office, on='Movie Title', how='inner')

merged_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1027 entries, 0 to 1026
Data columns (total 36 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   Movie Title         1027 non-null   object 
 1   3D_IMAX             1027 non-null   float64
 2   Sci-Fi              1027 non-null   float64
 3   Positive            1027 non-null   float64
 4   Action              1027 non-null   float64
 5   Horror              1027 non-null   float64
 6   Marketing           1027 non-null   float64
 7   K-Wave              1027 non-null   float64
 8   Biography           1027 non-null   float64
 9   Fantasy             1027 non-null   float64
 10  Oscars              1027 non-null   float64
 11  Box Office          1027 non-null   float64
 12  Co-Production       1027 non-null   float64
 13  Anticipation        1027 non-null   float64
 14  Viewing Habits      1027 non-null   float64
 15  Negative            1027 non-null   float64
 16  Neutral             1027 non-null   float64
 17  Superhero           1027 non-null   float64
 18  Script Development  1027 non-null   float64
 19  Kaiju               1027 non-null   float64
 20  Comedy              1027 non-null   float64
 21  Streaming           1027 non-null   float64
 22  Animation           1027 non-null   float64
 23  Filmmaking          1027 non-null   float64
 24  Political Themes    1027 non-null   float64
 25  Narrative           1027 non-null   float64
 26  Fake News           1027 non-null   float64
 27  IP Management       1027 non-null   float64
 28  Mixed               1027 non-null   float64
 29  Family Themes       1027 non-null   float64
 30  Social Diversity    1027 non-null   float64
 31  War                 1027 non-null   float64
 32  Viewing Trends      1027 non-null   float64
 33  CGI                 1027 non-null   float64
 34  Game Adaptation     1027 non-null   float64
 35  Box_Office_Amount   1027 non-null   int64  
dtypes: float64(34), int64(1), object(1)
memory usage: 289.0+ KB

# Load data
df = pd.read_csv('analysis_matrix.csv')  # LDA topic distributions
df1 = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv')  # Movie metadata

# --- Data Preparation ---
# Calculate average topic heat per movie
movie_topic_heat = df.groupby('Movie Title').mean().reset_index()

# Merge with box office data
movie_box_office = df1[['Title', 'Year', 'Box_Office_Amount']].rename(columns={'Title': 'Movie Title'})
merged_data = movie_topic_heat.merge(movie_box_office, on='Movie Title', how='inner')

# Print merged data for verification
print(merged_data.head())

           Movie Title     comment_id   3D_IMAX    Sci-Fi  Positive    Action  \
0                  '71  107712.186916  0.006703  0.045174  0.495938  0.052102   
1  10 Cloverfield Lane  104218.964851  0.000000  0.015935  0.456048  0.006159   
2            12 Strong  106520.241379  0.000000  0.044310  0.092825  0.030954   
3             13 Hours  109044.156118  0.000000  0.010192  0.482721  0.006711   
4                 1917  106995.405844  0.000000  0.031265  0.062317  0.023320   

     Horror  Marketing    K-Wave  Biography  ...     Mixed  Family Themes  \
0  0.018721   0.013177  0.007046   0.013875  ...  0.000000       0.000000   
1  0.001894   0.000000  0.000000   0.002158  ...  0.029797       0.009234   
2  0.011505   0.000000  0.000000   0.007291  ...  0.067678       0.040221   
3  0.002747   0.000000  0.000000   0.001747  ...  0.032255       0.007334   
4  0.001675   0.000000  0.000000   0.002179  ...  0.024848       0.005260   

   Social Diversity       War  Viewing Trends       CGI  Game Adaptation  \
0          0.000378  0.000606        0.008368  0.017411         0.050954   
1          0.000743  0.000898        0.013089  0.000994         0.003373   
2          0.000000  0.000000        0.000000  0.000000         0.000000   
3          0.002748  0.003482        0.025111  0.000360         0.000694   
4          0.002748  0.002395        0.039066  0.002991         0.006133   

   comment_year  Year  Box_Office_Amount  
0   2018.607477  2014            4377258  
1   2017.279438  2016          110216998  
2   2019.448276  2018           67033417  
3   2017.185654  2016           69411370  
4   2020.237013  2019          368027644  

[5 rows x 39 columns]

merged_data

	Movie Title	comment_id	3D_IMAX	Sci-Fi	Positive	Action	Horror	Marketing	K-Wave	Biography	...	Mixed	Family Themes	Social Diversity	War	Viewing Trends	CGI	Game Adaptation	comment_year	Year	Box_Office_Amount
0	'71	107712.186916	0.006703	0.045174	0.495938	0.052102	0.018721	0.013177	0.007046	0.013875	...	0.000000	0.000000	0.000378	0.000606	0.008368	0.017411	0.050954	2018.607477	2014	4377258
1	10 Cloverfield Lane	104218.964851	0.000000	0.015935	0.456048	0.006159	0.001894	0.000000	0.000000	0.002158	...	0.029797	0.009234	0.000743	0.000898	0.013089	0.000994	0.003373	2017.279438	2016	110216998
2	12 Strong	106520.241379	0.000000	0.044310	0.092825	0.030954	0.011505	0.000000	0.000000	0.007291	...	0.067678	0.040221	0.000000	0.000000	0.000000	0.000000	0.000000	2019.448276	2018	67033417
3	13 Hours	109044.156118	0.000000	0.010192	0.482721	0.006711	0.002747	0.000000	0.000000	0.001747	...	0.032255	0.007334	0.002748	0.003482	0.025111	0.000360	0.000694	2017.185654	2016	69411370
4	1917	106995.405844	0.000000	0.031265	0.062317	0.023320	0.001675	0.000000	0.000000	0.002179	...	0.024848	0.005260	0.002748	0.002395	0.039066	0.002991	0.006133	2020.237013	2019	368027644
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1022	Youth	106338.124122	0.000000	0.031442	0.273890	0.022757	0.008054	0.001235	0.000000	0.009672	...	0.106915	0.032719	0.006019	0.005507	0.065990	0.013526	0.019327	2021.067916	2015	24001573
1023	Zombieland: Double Tap	109281.948357	0.000000	0.091114	0.000000	0.058709	0.001291	0.000000	0.000000	0.000000	...	0.027087	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2019.098592	2019	122810399
1024	Zoolander 2	112731.720524	0.000000	0.016592	0.423961	0.004093	0.000000	0.000000	0.000000	0.000337	...	0.000000	0.001213	0.001966	0.001392	0.021981	0.001306	0.001731	2016.973799	2016	55969000
1025	Zootopia	111667.329466	0.000000	0.021964	0.422813	0.004645	0.000945	0.000000	0.000000	0.006754	...	0.017013	0.031920	0.003129	0.003908	0.031237	0.006054	0.010446	2018.619490	2016	1023784195
1026	xXx: Return of Xander Cage	124544.517857	0.000000	0.057609	0.000000	0.007229	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2017.160714	2017	346147658

1027 rows × 39 columns

# Load data
df = pd.read_csv('analysis_matrix.csv')  # LDA topic distributions
df1 = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv')  # Movie metadata

# --- Data Preparation ---
# Calculate average topic heat per movie
topic_columns = df.columns[1:-2]  # Exclude metadata columns
movie_topic_heat = df.groupby('Movie Title')[topic_columns].mean().reset_index()

# Merge with box office data
merged_data = movie_topic_heat.merge(
    df1[['Title', 'Box_Office_Amount']].rename(columns={'Title': 'Movie Title'}),
    on='Movie Title',
    how='inner'
)

# --- Correlation Analysis ---
# Calculate Spearman correlations
corr_results = []
for topic in topic_columns:
    r, p = stats.spearmanr(merged_data[topic], merged_data['Box_Office_Amount'])
    corr_results.append({'Topic': topic, 'Spearman_r': r, 'Spearman_p': p})

corr_df = pd.DataFrame(corr_results)
significant_topics = corr_df[
    (corr_df['Spearman_p'] < 0.01) & 
    (abs(corr_df['Spearman_r']) > 0.05)
]

# Print all results
print("="*80)
print("COMPLETE TOPIC CORRELATION ANALYSIS RESULTS:")
print(corr_df.sort_values('Spearman_r', ascending=False).to_string())
print("\nSIGNIFICANT TOPICS (p < 0.01 and |ρ| > 0.05):")
print(significant_topics.to_string())

# --- Superhero Special Analysis ---
superhero_data = corr_df[corr_df['Topic'] == 'Superhero'].iloc[0]
print("\n" + "="*30 + " SUPERHERO SPECIAL ANALYSIS " + "="*30)
print(f"Correlation: ρ = {superhero_data['Spearman_r']:.3f}, p = {superhero_data['Spearman_p']:.4f}")
if superhero_data['Spearman_p'] >= 0.01:
    print("Conclusion: No significant correlation with box office (p ≥ 0.01)")
elif abs(superhero_data['Spearman_r']) <= 0.05:
    print("Conclusion: Negligible effect size (|ρ| ≤ 0.05) despite significance")
else:
    print("Conclusion: Significant correlation detected")
print("="*80)

# --- Visualization Settings ---
sns.set_style("whitegrid", {'grid.linestyle': '--', 'grid.alpha': 0.3})
plt.rcParams['figure.figsize'] = (10, 5)
plt.rcParams['font.size'] = 12
color_palette = {'Positive': '#2ecc71', 'Negative': '#e74c3c'}

# --- Visualization Function ---
def plot_topic_effect(topic, data=merged_data):
    """Visualize the relationship between a topic and box office"""
    # Get correlation values
    r = corr_df.loc[corr_df['Topic'] == topic, 'Spearman_r'].values[0]
    p = corr_df.loc[corr_df['Topic'] == topic, 'Spearman_p'].values[0]
    direction = 'Positive' if r > 0 else 'Negative'
    
    # Create plot
    plt.figure()
    ax = sns.regplot(
        x=data[topic],
        y=np.log1p(data['Box_Office_Amount']),
        scatter_kws={'s': 50, 'alpha': 0.6, 'color': '#3498db'},
        line_kws={'color': color_palette[direction], 'linewidth': 2}
    )
    
    # Add annotations
    plt.title(f"Relationship: {topic} vs Box Office\nSpearman ρ = {r:.2f} (p = {p:.4f})")
    plt.xlabel(f"{topic} Topic Score")
    plt.ylabel("Log(Box Office Revenue)")
    
    # Add trend indicator
    plt.text(0.05, 0.92, f"{direction} Correlation", 
             transform=ax.transAxes,
             color=color_palette[direction],
             fontweight='bold',
             bbox=dict(facecolor='white', alpha=0.8))
    
    plt.tight_layout()
    plt.show()

# --- Generate Visualizations ---
print("\nVISUALIZING SIGNIFICANT TOPICS...")
for _, row in significant_topics.iterrows():
    plot_topic_effect(row['Topic'])

# --- Superhero Visualization (regardless of significance) ---
print("\nVISUALIZING SUPERHERO TOPIC (FOR REFERENCE)...")
plot_topic_effect('Superhero')

# --- Combined Summary Plot ---
print("\nGENERATING SUMMARY PLOT...")
top_effects = pd.concat([
    significant_topics.nlargest(3, 'Spearman_r'),  # Top 3 positive
    significant_topics.nsmallest(3, 'Spearman_r')   # Top 3 negative
])

plt.figure(figsize=(12, 6))
for i, (_, row) in enumerate(top_effects.iterrows()):
    plt.subplot(2, 3, i+1)
    sns.regplot(
        x=merged_data[row['Topic']],
        y=np.log1p(merged_data['Box_Office_Amount']),
        scatter_kws={'s': 30, 'alpha': 0.5},
        line_kws={'color': color_palette['Positive'] if row['Spearman_r'] > 0 else color_palette['Negative']}
    )
    plt.title(f"{row['Topic']}\nρ = {row['Spearman_r']:.2f}")
    plt.xlabel("")
    plt.ylabel("Log(BO)" if i % 3 == 0 else "")
    
plt.suptitle("Top 3 Positive and Negative Correlations with Box Office", y=1.02)
plt.tight_layout()
plt.show()

================================================================================
COMPLETE TOPIC CORRELATION ANALYSIS RESULTS:
                 Topic  Spearman_r  Spearman_p
18               Kaiju    0.128148    0.000038
2             Positive    0.078592    0.011753
30                 War    0.076107    0.014705
29    Social Diversity    0.072738    0.019738
31      Viewing Trends    0.067936    0.029482
1               Sci-Fi    0.060575    0.052299
33     Game Adaptation    0.049444    0.113296
32                 CGI    0.049386    0.113714
21           Animation    0.042952    0.168997
0              3D_IMAX    0.036327    0.244776
6               K-Wave    0.033260    0.286937
5            Marketing    0.017128    0.583502
3               Action    0.009838    0.752828
19              Comedy   -0.001972    0.949668
16           Superhero   -0.006135    0.844332
7            Biography   -0.010383    0.739627
13      Viewing Habits   -0.019827    0.525644
8              Fantasy   -0.023569    0.450558
28       Family Themes   -0.025285    0.418249
17  Script Development   -0.033001    0.290705
11       Co-Production   -0.039439    0.206646
26       IP Management   -0.049575    0.112339
23    Political Themes   -0.060773    0.051534
4               Horror   -0.064511    0.038733
14            Negative   -0.067475    0.030604
25           Fake News   -0.068036    0.029242
24           Narrative   -0.069202    0.026577
27               Mixed   -0.073050    0.019217
22          Filmmaking   -0.077944    0.012468
20           Streaming   -0.080441    0.009911
12        Anticipation   -0.106083    0.000661
9               Oscars   -0.114771    0.000228
15             Neutral   -0.121012    0.000101
10          Box Office   -0.122577    0.000082

SIGNIFICANT TOPICS (p < 0.01 and |ρ| > 0.05):
           Topic  Spearman_r  Spearman_p
9         Oscars   -0.114771    0.000228
10    Box Office   -0.122577    0.000082
12  Anticipation   -0.106083    0.000661
15       Neutral   -0.121012    0.000101
18         Kaiju    0.128148    0.000038
20     Streaming   -0.080441    0.009911

============================== SUPERHERO SPECIAL ANALYSIS ==============================
Correlation: ρ = -0.006, p = 0.8443
Conclusion: No significant correlation with box office (p ≥ 0.01)
================================================================================

VISUALIZING SIGNIFICANT TOPICS...

VISUALIZING SUPERHERO TOPIC (FOR REFERENCE)...

GENERATING SUMMARY PLOT...

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np

df = pd.read_csv('analysis_matrix.csv')

df

	comment_id	3D_IMAX	Sci-Fi	Positive	Action	Horror	Marketing	K-Wave	Biography	Fantasy	...	IP Management	Mixed	Family Themes	Social Diversity	War	Viewing Trends	CGI	Game Adaptation	Movie Title	comment_year
0	5	0.015880	0.190231	0.381898	0.271167	0.029295	0.030989	0.039260	0.022075	0.019205	...	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	The Lego Movie	2014
1	24	0.000000	0.000000	0.570520	0.365656	0.000000	0.000000	0.011750	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	The Amazing Spider-Man 2	2014
2	58	0.012435	0.042597	0.507401	0.217727	0.022941	0.024267	0.030980	0.126612	0.015040	...	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	Sex Tape	2014
3	60	0.015849	0.191520	0.381149	0.133607	0.029238	0.168173	0.039183	0.022032	0.019250	...	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	Transformers: Age of Extinction	2014
4	101	0.000000	0.032189	0.700770	0.174051	0.017595	0.018338	0.023232	0.013063	0.011365	...	0.0	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	Gone Girl	2014
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
222515	222477	0.000000	0.032327	0.293647	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.050286	0.097025	Dune: Part Two	2024
222516	222483	0.000000	0.026527	0.241635	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.041262	0.079604	Kung Fu Panda 4	2024
222517	222503	0.000000	0.030125	0.272317	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.046858	0.090400	It Follows	2024
222518	222511	0.000000	0.022501	0.310642	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.186873	0.067580	It	2024
222519	222519	0.000000	0.043725	0.209369	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.0	0.079745	0.039508	Amy	2024

222520 rows × 37 columns

df1 = pd.read_csv('/Users/heshuqing0305/Downloads/new_data.csv')

df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1095 entries, 0 to 1094
Data columns (total 11 columns):
 #   Column             Non-Null Count  Dtype  
---  ------             --------------  -----  
 0   Title              1095 non-null   object 
 1   Year               1095 non-null   int64  
 2   Director           1095 non-null   object 
 3   Rating             1095 non-null   float64
 4   Votes              1095 non-null   int64  
 5   Genres             1095 non-null   object 
 6   Actors             1095 non-null   object 
 7   Languages          1095 non-null   object 
 8   Countries          1095 non-null   object 
 9   Budget_Amount      1095 non-null   int64  
 10  Box_Office_Amount  1095 non-null   int64  
dtypes: float64(1), int64(4), object(6)
memory usage: 94.2+ KB

df = df.rename(columns=lambda x: x.strip())
df1 = df1.rename(columns=lambda x: x.strip())

df['Movie Title'] = df['Movie Title'].str.strip()
df1['Movie Title'] = df1['Title'].str.strip()

merged_df = pd.merge(df, df1[['Movie Title', 'Box_Office_Amount']], on='Movie Title', how='left')

merged_df

	comment_id	3D_IMAX	Sci-Fi	Positive	Action	Horror	Marketing	K-Wave	Biography	Fantasy	...	Mixed	Family Themes	Social Diversity	War	Viewing Trends	CGI	Game Adaptation	Movie Title	comment_year	Box_Office_Amount
0	5	0.015880	0.190231	0.381898	0.271167	0.029295	0.030989	0.039260	0.022075	0.019205	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	The Lego Movie	2014	468760692
1	24	0.000000	0.000000	0.570520	0.365656	0.000000	0.000000	0.011750	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	The Amazing Spider-Man 2	2014	708982323
2	58	0.012435	0.042597	0.507401	0.217727	0.022941	0.024267	0.030980	0.126612	0.015040	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	Sex Tape	2014	126069509
3	60	0.015849	0.191520	0.381149	0.133607	0.029238	0.168173	0.039183	0.022032	0.019250	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	Transformers: Age of Extinction	2014	1104039076
4	101	0.000000	0.032189	0.700770	0.174051	0.017595	0.018338	0.023232	0.013063	0.011365	...	0.0	0.0	0.0	0.0	0.0	0.000000	0.000000	Gone Girl	2014	369330363
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
232293	222483	0.000000	0.026527	0.241635	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.041262	0.079604	Kung Fu Panda 4	2024	547689492
232294	222503	0.000000	0.030125	0.272317	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.046858	0.090400	It Follows	2024	23236296
232295	222511	0.000000	0.022501	0.310642	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.186873	0.067580	It	2024	700381748
232296	222511	0.000000	0.022501	0.310642	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.186873	0.067580	It	2024	700381748
232297	222519	0.000000	0.043725	0.209369	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.0	0.0	0.0	0.0	0.0	0.079745	0.039508	Amy	2024	23778585

232298 rows × 38 columns

# Step 1: Calculate the average topic heat for each year
avg_topic_distribution = merged_df.groupby(['Movie Title', 'comment_year'])[['Kaiju']].mean().reset_index()

# Step 2: Extract unique box office data for each movie
unique_box_office = merged_df[['Movie Title', 'comment_year', 'Box_Office_Amount']].drop_duplicates()

# Step 3: Merge average topic heat with box office data
final_data = pd.merge(avg_topic_distribution, unique_box_office, on=['Movie Title', 'comment_year'], how='left')

# Step 4: Plot the line chart
plt.figure(figsize=(14, 7))

# Create the first y-axis: topic heat
sns.lineplot(data=final_data, x='comment_year', y='Kaiju', label='Kaiju', marker='o', color='blue')

# Create the second y-axis: box office
ax2 = plt.gca().twinx()  # Create a second y-axis sharing the x-axis
sns.lineplot(data=final_data, x='comment_year', y='Box_Office_Amount', label='Box Office', marker='o', color='green', ax=ax2)

# Set title and labels
plt.title('Average Topic Distribution and Box Office Amount Over Years')
plt.xlabel('Year')
plt.ylabel('Average Topic Distribution')
ax2.set_ylabel('Box Office Amount')

# Add legends
plt.legend(loc='upper left')
ax2.legend(loc='upper right')

plt.xticks(rotation=45)
plt.grid()
plt.show()

avg_topic_distribution = merged_df.groupby(['Movie Title', 'comment_year'])[['Superhero']].mean().reset_index()

# Step 2: Extract box office data related to Superhero
# Select box office data for movies related to the Superhero theme
relevant_movies = merged_df[merged_df['Superhero'] > 0][['Movie Title', 'comment_year', 'Box_Office_Amount']].drop_duplicates()

# Step 3: Merge average topic heat with box office data
final_data = pd.merge(avg_topic_distribution, relevant_movies, on=['Movie Title', 'comment_year'], how='left')

# Step 4: Plot the line chart
plt.figure(figsize=(14, 7))

# Create the first y-axis: topic heat
sns.lineplot(data=final_data, x='comment_year', y='Superhero', label='Superhero', marker='o', color='red')

# Create the second y-axis: box office
ax2 = plt.gca().twinx()  # Create a second y-axis sharing the x-axis
sns.lineplot(data=final_data, x='comment_year', y='Box_Office_Amount', label='Box Office', marker='o', color='green', ax=ax2)

# Set title and labels
plt.title('Average Superhero Topic Distribution and Box Office Amount Over Years')
plt.xlabel('Year')
plt.ylabel('Average Superhero Topic Distribution')
ax2.set_ylabel('Box Office Amount')

# Add legends
plt.legend(loc='upper left')
ax2.legend(loc='upper right')

plt.xticks(rotation=45)
plt.grid()
plt.show()